{"id":13811102,"url":"https://github.com/matthewvowels1/Awesome-Causal-Inference","last_synced_at":"2025-05-14T15:31:35.171Z","repository":{"id":41502248,"uuid":"264424460","full_name":"matthewvowels1/Awesome-Causal-Inference","owner":"matthewvowels1","description":"A curated list of awesome work on causal inference, particularly in machine learning.","archived":false,"fork":false,"pushed_at":"2021-05-01T09:36:48.000Z","size":19,"stargazers_count":103,"open_issues_count":0,"forks_count":17,"subscribers_count":9,"default_branch":"master","last_synced_at":"2025-05-08T08:02:35.394Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/matthewvowels1.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-05-16T11:43:34.000Z","updated_at":"2025-05-07T19:23:04.000Z","dependencies_parsed_at":"2022-08-10T02:27:32.395Z","dependency_job_id":null,"html_url":"https://github.com/matthewvowels1/Awesome-Causal-Inference","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthewvowels1%2FAwesome-Causal-Inference","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthewvowels1%2FAwesome-Causal-Inference/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthewvowels1%2FAwesome-Causal-Inference/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/matthewvowels1%2FAwesome-Causal-Inference/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/matthewvowels1","download_url":"https://codeload.github.com/matthewvowels1/Awesome-Causal-Inference/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254171873,"owners_count":22026532,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-04T03:00:37.811Z","updated_at":"2025-05-14T15:31:30.130Z","avatar_url":"https://github.com/matthewvowels1.png","language":null,"funding_links":[],"categories":["Other Awesome Repos","🚀 GitHub Repositories","Other Awesome List","Machine Learning \u0026 AI","Data Analysis"],"sub_categories":["🌟 **Real-World Magic**","Causal Discovery"],"readme":"# Awesome-Causal-Inference\nA curated list of awesome work on causal inference and (some) causal discovery, particularly in machine learning.\n\nI am gathering resources (currently @ 271 papers) as literature for my PhD, and thought it may come in useful for others. This list includes work relating causal inference to deep learning, statistics, machine learning, and representation learning. If I've missed your paper, or there's a paper you want on the list, then feel free to contribute or email me : ]\n\nThe are ordered by year (new to old).\n\n## 2021\n\n\nIdentification of Latent Variables From Graphical Model Residuals. Hayete, Gruber, Decker, Yan. https://arxiv.org/pdf/2101.02332.pdf\n\nDisentangling Observed Causal Effects from Latent Confounders using Method of Moments. Liu, Liu, Li, Karimi-Bidhendi, Yue, Anandkumar. https://arxiv.org/pdf/2101.06614.pdf\n\nCounterfactual Generative Networks. Sauer, Geiger. https://arxiv.org/pdf/2101.06046.pdf\n\nModel Compression for Domain Adaptation through Causal Effect Estimation. Rotman, Feder, Reichart.\nhttps://arxiv.org/pdf/2101.07086.pdf\n\nDiscrete Graph Structure Learning for Forecasting Multiple Time Series. Shang, Chen, Bi. https://arxiv.org/pdf/2101.06861.pdf\n\nInstance-Specific Causal Bayesian Network Structure Learning. Jabbari. http://d-scholarship.pitt.edu/40018/19/Jabbari%20Final%20ETD.pdf\n\nThe limits of graphical causal discovery. Sevilla. https://towardsdatascience.com/the-limits-of-graphical-causal-discovery-92d92aed54d6?gi=db403d386344\n\nEstimating Average Treatment Effects via Orthogonal Regularization. Hatt, Feuerriegel. https://arxiv.org/pdf/2101.08490.pdf\n\nCDSM--Casual Inference using Deep Bayesian Dynamic Survival Models. Zhu, Gallego. https://arxiv.org/pdf/2101.10643.pdf\n\nNonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. Curth, van der Schaar. https://arxiv.org/pdf/2101.10943.pdf\n\nCausality and independence in perfectly adapted dynamical systems. Blom, Mooij. https://arxiv.org/pdf/2101.11885.pdf\n\nCausal inference for quantile treatment effects. Sun, Moodie, Neslehova. https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2668\n\nVariational Bayes survival analysis for unemployment modelling. Boskoski, Perne, Ramesa, Boshkoska. https://arxiv.org/pdf/2102.02295.pdf\n\nA scoping review of causal methods enabling predictions under hypothetical interventions. Lin, Sperrin, Jenkins, Martin, Peek. https://link.springer.com/article/10.1186/s41512-021-00092-9\n\nEstimating the treatment effect for adherers using multiple imputation. Luo, Ruberg, Qu. https://arxiv.org/pdf/2102.03499.pdf\n\nOn the Sample Complexity of Causal Discovery and the Value of Domain Expertise. Wadhwa, Dong. https://arxiv.org/pdf/2102.03274.pdf\n\nInteger Programming for Causal Structure Learning in the Presence of Latent Variables. Chen, Dash, Gao. https://arxiv.org/pdf/2102.03129.pdf\n\nImproving Causal Discovery By Optimal Bayesian Network Learning. Lu. Zhang, Yuan. https://www.aaai.org/AAAI21Papers/AAAI-8537.LuN.pdf\n\nEstimating Identifiable Causal Effects through Double Machine Learning. Jung, Tian, Bareinboim. https://www.aaai.org/AAAI21Papers/AAAI-8987.JungY.pdf\n\nCounterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction. Nguyen, Quinn, Nguyen, Tran. https://arxiv.org/pdf/2103.12983.pdf\n\nUser-oriented smart general AI system under causal inference. Peng. https://arxiv.org/pdf/2103.14561.pdf\n\nA New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model. Ying, Tchetgen. https://arxiv.org/pdf/2103.12206.pdf\n\nAverage Treatment Effects in the Presence of Interference. Hu, Li, Wager. https://arxiv.org/pdf/2104.03802.pdf\n\nFRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. Chen, Zhang, Cai, Huang, Ramsey, Hao, Glymour. https://arxiv.org/pdf/2103.14238.pdf\n\nA brief introduction to causal inference. Nguyen. http://review.ttu.edu.vn/index.php/review/article/download/112/120\u0026hl=en\u0026sa=X\u0026d=3868739188093254491\u0026ei=S3B1YKqkKPqB6rQP7YGXiAI\u0026scisig=AAGBfm2BnYjxbqbGRyi2ySV4VOsV383PQg\u0026nossl=1\u0026oi=scholaralrt\u0026html=\u0026folt=cit\n\nCausal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. Fernandez-Loria, Provost. https://arxiv.org/pdf/2104.04103.pdf\n\nBeing bayesian about causal inference. Bucur. https://repository.ubn.ru.nl/bitstream/handle/2066/226922/226922.pdf?sequence=1\u0026isAllowed=y\n\nA computational model for complex systems analysis: Causality estimation. Sinha, Loparo. https://www.sciencedirect.com/science/article/abs/pii/S0167278921000737\n\nPost-selection Problems for Causal Inference with Invalid Instruments: A Solution Using Searching and Sampling. Guo. https://arxiv.org/pdf/2104.06911.pdf\n\nOn the implied weights of linear regression for causal inference. Chattopadhyay, Zubizarreta. https://arxiv.org/pdf/2104.06581.pdf\n\nFast and effective pseudo transfer entropy for bivariate data-driven causal inference. Silini, Masoller. https://www.nature.com/articles/s41598-021-87818-3\n\nShadow-Mapping for Unsupervised Neural Causal Discovery. Vowels, Camgoz, Bowden. https://arxiv.org/pdf/2104.08183.pdf\n\nSequential Deconfounding for Causal Inference with Unobserved Confounders. Hatt, Feuerriegel. https://arxiv.org/pdf/2104.09323.pdf\n\nCATE meets ML-The Conditional Average Treatment Effect and Machine Learning. Jacob.  https://arxiv.org/pdf/2104.09935.pdf\n\nA calculus for causal inference with instrumental variables. Wong. https://arxiv.org/pdf/2104.10633.pdf\n\nCausal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networks. Wen, Colon, Subbalakshmi, Chandramouli. https://arxiv.org/pdf/2104.10680.pdf\n\n\nCausal Discovery. Sucar https://link.springer.com/chapter/10.1007/978-3-030-61943-5_15\n\nUnderstanding the causal structure among the tags in marketing systems. Zheng, Yang, Liu https://link.springer.com/article/10.1007/s00521-020-05552-9\n\nNonlinear Invariant Risk Minimization: A Causal Approach. Lu.Wu, Hernandez-Lobato, Scholkopf https://arxiv.org/pdf/2102.12353.pdf\n\nBeware of the Simulated DAG! Varsortability in Additive Noise Models. Reisach, Seiler, Weichwald https://arxiv.org/pdf/2102.13647.pdf\n\nCovariate balancing for causal inference on categorical and continuous treatments. Lee, Ma, de Luna\nhttps://arxiv.org/pdf/2103.00527.pdf\n\nToward Causal Representation Learning. Scholkopf, Locatello, Bauer, Ke, Kalchbrenner, Goyal, Bengio https://ieeexplore.ieee.org/document/9363924/?denied=\n\nImproving Causal Inference by Increasing Model Expressiveness. Jensen https://www.aaai.org/AAAI21Papers/SMT-427.JensenD.pdf\n\nA Generative Adversarial Framework for Bounding Confounded Causal Effects. Hu, Wu, Zhang, Wu https://www.aaai.org/AAAI21Papers/AAAI-3651.HuY.pdf\n\nWhy did the distribution change? Budhathoki, Janzing, Blobaum, Ng https://arxiv.org/pdf/2102.13384.pdf\n\nIncorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation. Teshima, Sugiyama https://arxiv.org/pdf/2103.00136.pdf\n\nRegularizing towards Causal Invariance: Linear Models with Proxies. Oberst, Thams, Peters, Sontag https://arxiv.org/pdf/2103.02477.pdf\n\nRelate and predict: Structure-Aware prediction with Jointly Optimized Neural DAG. Sekhon, Wang, Qi https://arxiv.org/pdf/2103.02405.pdf\n\nEstimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. Jung, Tian, Bareinboim https://causalai.net/r71.pdf\n\nD?ya like DAGs? A Survey on Structure Learning and Causal Discovery. Vowels, Camgoz, Bowden https://arxiv.org/pdf/2103.02582.pdf\n\nNon-Parametric Methods for Partial Identification of Causal Effects. Zhang, Bareinboim https://causalai.net/r72.pdf\n\nPlacebo Tests for Causal Inference. Eggers, Tunon, Dafoe https://pelg.ucsd.edu/Eggers_2021.pdf\n\nBayesian Doubly Robust Causal Inference via Loss Functions. Luo, Stephens, Graham, McCoy https://arxiv.org/pdf/2103.04086.pdf\n\nCausality indices for bivariate time series data: a comparative review of performance. Edinburgh, Eglen, Ercole https://arxiv.org/pdf/2104.00718.pdf\n\nDoubly robust confidence sequences for sequential causal inference. Waudby-Smith, Arbour, Sinha, Kennedy, Ramdas https://arxiv.org/pdf/2103.06476.pdf\n\nQuantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. Jesson, Mindermann, Gal, Shalit https://arxiv.org/pdf/2103.04850.pdf\n\nA Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. Tan, Chang, Tang https://arxiv.org/pdf/2103.06261.pdf\n\nTime-Reversibility, Causality and Compression-Complexity. Kathpalia, Nagaraj.\n\nIdentifiability of causal effects with multiple causes and a binary outcome. Kong, Yang, Wang https://academic.oup.com/biomet/advance-article-abstract/doi/10.1093/biomet/asab016/6168988\n\nThree Essays on Model Selection in Time Series Econometrics. Aka  https://refubium.fu-berlin.de/handle/fub188/29740\n\nCausal Inference Q-Network: Toward Resilient Reinforcement Learning. Yang, Hung, Ouyang, Chen https://arxiv.org/pdf/2102.09677.pdf\n\nGenerating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. Schut, Key, McGrath, Costabello, Sacaleanu, Corcoran, Gal https://arxiv.org/pdf/2103.08951.pdf\n\nTreatment Effect Estimation using Invariant Risk Minimization. Shah, Ahuja, Shanmugam, Wei, Varshney, Dhurandhar https://arxiv.org/pdf/2103.07788.pdf\n\nVCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. Nie, Ye, Liu, Nicolae https://arxiv.org/pdf/2103.07861.pdf\n\n\nUplift Modeling: from Causal Inference to Personalization. Teinemaa, Albert, Goldenberg https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762341_Uplift_Modeling_from_Causal_Inference_to_Personalization/links/60409ccca6fdcc9c780f8b37/Uplift-Modeling-from-Causal-Inference-to-Personalization.pdf\n\n\nHigher-Order Orthogonal Causal Learning for Treatment Effect. Huang, Leung, Yan, Wu  https://arxiv.org/pdf/2103.11869.pdf\n\nNCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments. Parbhoo, Bauer, Schwab https://arxiv.org/pdf/2103.11175.pdf\n\nCausal Inference under Networked Interference and Intervention Policy Enhancement. Ma, Tresp  http://proceedings.mlr.press/v130/ma21c/ma21c.pdf\n\nCausal Modeling with Stochastic Confounders. Vo, Wei, Bergsma, Leong http://proceedings.mlr.press/v130/vinh-vo21a/vinh-vo21a.pdf\n\nANOCE: analysis of causal effects with multiple mediators via constrained structural learning.  Cai, Song, Lu https://openreview.net/pdf/8142413a92e7df5fb79598dee863640346d53f5b.pdf\n\nCausal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. Kallus, Mao, Uehara https://arxiv.org/pdf/2103.14029.pdf\n\nConditions and Assumptions for Constraint-based Causal Structure Learning. Sadeghi, Soo  https://arxiv.org/pdf/2103.13521.pdf\n\nRobust estimation of heterogeneous treatment effects using electronic health record data. Li, Wang, Tu https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8926\n\nCausal Discovery with Bijective Fixed-Cause Functionals. Jalaldoust, Salehkaleybar, Kiyavash. http://jalaldoust.com/docs/CDBFF.pdf\n\nUser-Oriented Smart General AI System under Causal Inference.  Peng https://arxiv.org/pdf/2103.14561.pdf\n\nDeconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding.  Bellot, van der Schaar https://arxiv.org/pdf/2103.15106.pdf\n\nA New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model. Ying, Tchetgen  https://arxiv.org/pdf/2103.12206.pdf\n\nSEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models. Paluzzi, Grassi https://arxiv.org/pdf/2103.08332.pdf\n\nBayesian optimal experimental design for inferring causal structure.  Zemplenyi, Miller https://arxiv.org/pdf/2103.15229.pdf\n\nMulti-Source Causal Inference Using Control Variates.  Guo, Wang, Ding, Wang, Jordan  https://arxiv.org/pdf/2103.16689.pdf\n\nIntact-VAE: Estimating Treatment Effects under Unobserved Confounding.  Wu, Fukumizu  https://arxiv.org/pdf/2101.06662.pdf\n\n## 2020\n\nThe Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods. Adams, Saria, Rosenblum. https://arxiv.org/pdf/2011.15099.pdf\n\nalgcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD. Ramsey, Malinsky, Bui https://www.jmlr.org/papers/volume21/19-773/19-773.pdf\n\nTowards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case. Neto https://www.cmu.edu/dietrich/causality/CameraReadys-accepted%20papers/23%5CCameraReady%5Ccdml_causality_aware.pdf\n\nEvaluation of Algorithm Selection and Ensemble Methods for Causal Discovery. Saldanha, Cosbey, Ayton, Glenski, Cottam, Shivaram, Jefferson, Hutchinson, Arendt, Volkova https://www.cmu.edu/dietrich/causality/CameraReadys-accepted%20papers/28%5CCameraReady%5CEvaluating_Causal_Ensembles_NeurIPS_CR.pdf\n\nCausal World Models by Unsupervised Deconfounding of Physical Dynamics. Li, Yang, Liu, Chen, Chen, Wang https://arxiv.org/pdf/2012.14228.pdf\n\nIntervention Efficient Algorithms for Approximate Learning of Causal Graphs. Addanki, McGregor, Musco\nhttps://arxiv.org/pdf/2012.13976.pdf\n\nAmortized learning of neural causal representations.\tKe, Wang, Mitrovic, Szummer, Rezende\thttps://arxiv.org/pdf/2008.09301.pdf\n\nCausal future prediction in a Minkowski space-time.\tVlontzos, Rocha, Rueckert, Kainz\thttps://arxiv.org/pdf/2008.09154.pdf\n\nPath dependent structural equation models.\tSrinivasan, Lee, Ahmidi, Shpitser\thttps://arxiv.org/pdf/2008.10706.pdf\n\nA narrative review of methods for causal inference and associated educational resources. \tLandsittel, Srivastava, Kropf, Kristin\thttps://journals.lww.com/qmhcjournal/Abstract/2020/10000/A_Narrative_Review_of_Methods_for_Causal_Inference.12.aspx?context=LatestArticles\n\nTargeted VAE: structured inference and targeted learning for causal parameter estimation.\tVowels, Camgoz, Bowden\thttps://arxiv.org/pdf/2009.13472.pdf\n\nCASTLE: regularization via auxiliary causal graph discovery.\tKyono, Zhang\thttps://arxiv.org/pdf/2009.13180.pdf\n\nIdentifying treatment effects under unobserved confounding by causal representation learning.\tAnonymous\thttps://openreview.net/forum?id=D3TNqCspFpM\n\nCausal discovery for causal bandits utilizing separating sets.\tde Kroon, Belgrave, Mooij\thttps://arxiv.org/pdf/2009.07916.pdf\n\nLearning DAGs with continuous optimization.\tZheng\thttps://www.ml.cmu.edu/research/phd-dissertation-pdfs/thesis-zheng-xun.pdf\n\t\t\nHybridizing machine learning methods and finite mixture models for estimating heterogeneous treatment effects in latent classes.\tSuk, Kim, Kang\t https://journals.sagepub.com/doi/abs/10.3102/1076998620951983\n\nTuning causal discovery algorithms.\tBiza, Tsamardinos, Triantafillou\t https://pgm2020.cs.aau.dk/wp-content/uploads/2020/09/biza20.pdf\n\nDebiased machine learning of conditional average treatment effects and other causal functions.\tSemenova, Chernozhukov\thttps://academic.oup.com/ectj/advance-article-abstract/doi/10.1093/ectj/utaa027/5899048\n\nEstimating individual treatment effects with time-varying confounders.\tLiu, Yin, Zhang\thttps://arxiv.org/pdf/2008.13620.pdf\n\nConfounding feature acquisition for causal effect estimation.\tWang, Yi, Joshi, Ghassemi\thttps://arxiv.org/pdf/2011.08753.pdf\n\nCausal inference methods for combining randomized trials and observational studies: a review. \tColnet, Mayer, Chen, Dieng, Li, Varoquaux, Vert, Josse, Yang\thttps://arxiv.org/pdf/2011.08047.pdf\n\nReconstruction of a directed acyclic graph with intervention.\tPeng, Shen\thttps://projecteuclid.org/download/pdfview_1/euclid.ejs/1605582080\n\nDebiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders.\tWang, Shah\thttps://arxiv.org/pdf/2011.08661.pdf\n\nA novel method for Causal Structure Discovery from EHR data.  Shen, Ma, Vemuri, Castro, Caraballo, Simon\thttps://arxiv.org/pdf/2011.05489.pdf\n\nTeaching deep learning causal effects improves predictive performance.\tLi, Jia, Yang, Kumar, Steinbach, Simon\thttps://arxiv.org/pdf/2011.05466.pdf\n\nLearning Causal Representations for Robust Domain Adaptation.\tYang, Yu, Cao, Liu, Wang, Li\thttps://arxiv.org/pdf/2011.06317.pdf   \n\nLearning causal semantic representations for out-of-distribution prediction. Liu, Sun, Wang, Li, Qin, Chen, Liu\thttps://arxiv.org/pdf/2011.01681.pdf\n\n\nCounterfactual Fairness with  disentangled causal effect variational autoencoder.\tKim, Shin, Jang, Song, Joo, Kang, Moon\thttps://arxiv.org/pdf/2011.11878.pdf\n\nA systematic review of causal methods enabling predictions under hypothetical interventions.\tLin, Sperrin, Jenkins, Martin, Peek\thttps://arxiv.org/pdf/2011.09815.pdf\n\nEfficient permutation discovery in causal DAGs. \tSquires, Amaniampong, Uhler\thttps://arxiv.org/pdf/2011.03610.pdf\n\nCausality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners.\tNeto\thttps://arxiv.org/pdf/2011.04605.pdf\n\nConditional independence testing for variable selection and causal inference.\tBates\thttps://search.proquest.com/openview/f46a5071aecc21df0cbb3f43d408bcfd/1?pq-origsite=gscholar\u0026cbl=18750\u0026diss=y\n\nInterpretable models for Granger causality using self-explaining neural networks.\tMarcinkevics, Vogt\thttps://mds.inf.ethz.ch/fileadmin/user_upload/gc_neurips2020_workshop_cr.pdf\n\nHigh-dimensional feature selection for sample efficient treatment effect estimation.\tGreenewald, Katz-Rogozhnikov, Shanmugam\thttps://arxiv.org/pdf/2011.01979.pdf\n\nLatent causal invariant model.\tSun, Wu, Liu, Zheng, Chen, Qin, Liu\thttps://arxiv.org/pdf/2011.02203.pdf\n\nApplications of common entropy for causal inference.\tKocaoglu, Shakkottai, Dimakis, Caramanis, Vishwanath\thttps://proceedings.neurips.cc/paper/2020/file/cae7115f44837c806c9b23ed00a1a28a-Paper.pdf\n\nEntropic causal inference: identifiability and finite sample results.\tCompton, Kocaoglu, Greenewald, Katz\thttps://proceedings.neurips.cc/paper/2020/file/a979ca2444b34449a2c80b012749e9cd-Paper.pdf\n\nGeneralized independent noise condition for estimating latent variable causal graphs.\tXie, Cai, Huang, Glymour, Hao, Zhang\thttps://proceedings.neurips.cc/paper/2020/file/aa475604668730af60a0a87cc92604da-Paper.pdf\n\nBayesian causal structural learning with zero-inflated poisson bayesian networks.\tChoi, Chapkin, Ni\thttps://proceedings.neurips.cc/paper/2020/file/4175a4b46a45813fccf4bd34c779d817-Paper.pdf\n\nCausal autoregressive flows.\tKhemakhem, Monti, Leech, Hyvarinen\thttps://arxiv.org/pdf/2011.02268.pdf\n\nCausal variables from reinforcement learning using generalized Bellman equations.\tHerlau\thttps://arxiv.org/pdf/2010.15745.pdf\n\nDomain adaptation under structural causal models.\tChen, Buhlmann\thttps://arxiv.org/pdf/2010.15764.pdf\n\nCausalworld: A robotic manipulation benchmark for causal structure and transfer learning.\t  Ahmed, Trauble, Goyal, Neitz, Bengio, Scholkopf, Bauer, Wuthrich\thttps://arxiv.org/pdf/2010.04296.pdf\n\nRepresentation learning for treatment effect estimation.\tYao\thttps://search.proquest.com/openview/d21f343b17412c1af8099ac93ae92fee/1?pq-origsite=gscholar\u0026cbl=18750\u0026diss=y\n\nNeural additive vector autoregression models for causal discovery in time series data.\tBussmann, Nys, Latre\thttps://arxiv.org/pdf/2010.09429.pdf\n\nCausal discovery using compression-complexity measures.\tSY, Nagaraj\thttps://arxiv.org/pdf/2010.09336.pdf\n\nDAGs with no fears: a closer look at continuous optimization for learning bayesian networks.\tWei, Gao, Yu\thttps://arxiv.org/pdf/2010.09133.pdf\n\nLearning robust models using the principle of independent causal mechanisms. \tMuller, Schmier, Ardizzone, Rother, Kothe\thttps://arxiv.org/pdf/2010.07167.pdf\n\nDouble robust representation learning for counterfactual prediction.\tZeng, Asaad, Tao, Datta, Carin, Li\thttps://arxiv.org/pdf/2010.07866.pdf\n\nCausal learning with sufficient statistics: an information bottleneck approach.\tChicharro, Besserve, Panzeri\thttps://arxiv.org/pdf/2010.05375.pdf\n\nDifferentiable causal discovery under unmeasured confounding.\tBhattacharya, Nagarajan, Malinsky, Shpitser\thttps://arxiv.org/pdf/2010.06978.pdf\n\nIdentifying causal-effect inference failure with uncertainty-aware models.\tJesson, Mindermann, Shalit, Gal\thttps://arxiv.org/abs/2007.00163\n\nCausally correct partial models for reinforcement learning.\tRezende, Danihelka, Papamakarios, Ke, Jiang, Webever, Gregor, Merzic, Viola, Wang, Mitrovic, Besse, Antonoglou, Buesing\thttps://arxiv.org/pdf/2002.02836v1.pdf\n\nCausal curiosity: RL agents discovering self-supervised experiments for causal representation learning.\tSontakke, Mehrjou\thttps://arxiv.org/pdf/2010.03110.pdf\n\nAssessing the fairness of classifiers with collider bias.\tXi, Liu, Cheng, Li, Liu, Kang\thttps://arxiv.org/pdf/2010.03933.pdf\n\nDisentangling causal effects for hierarchical reinforcement learning.\tCorcoll. Vicente\thttps://arxiv.org/pdf/2010.01351.pdf\n\nA new representation learning method for individual treatment effect estimation: split covariate representation network.\tLiu, Tian, Ji, Zheng\thttp://proceedings.mlr.press/v129/qidong20a/qidong20a.pdf\n\nPersonalized estimation and causal inference via deep learning algorithms.\tLiu\thttps://digitalcommons.library.tmc.edu/cgi/viewcontent.cgi?article=1149\u0026context=uthsph_dissertsopen\n\nDisentangled generative causal representation learning.\tShen, Liu, Dong, Lian, Chen, Zhang\thttps://arxiv.org/pdf/2010.02637.pdf\n\nExplaining the efficacy of counterfactually-augmented data.\tKaushik, Setlur, Hovy, Lipton\thttps://arxiv.org/pdf/2010.02114.pdf\n\nA new framework for causal discovery.\tvan Leeuwen, DeCaria, Chakaborty, Pulido\thttps://arxiv.org/pdf/2010.02247.pdf\n\nGraphical Granger causality by information-theoretic criteria.\tHlavackova-Schindler, Plant\thttp://eprints.cs.univie.ac.at/6518/1/264_paper.pdf\n\nSystematic evaluation of causal discovery in visual model based reinforcement learning\tanonymous\thttps://openreview.net/pdf/fd60f3b99ed8b26cd60f5f884fe2e6eb7e3ec327.pdf\n\nLong-term effect estimation with surrogate representation. \tCheng, Guo, Liu\thttps://arxiv.org/pdf/2008.08236.pdf\n\nHeidegger: Interpretable temporal causal discovery.\tMansouri, Arab, Zohrevand, Ester\thttps://dl.acm.org/doi/abs/10.1145/3394486.3403220\n\nDeconfounding and causal regularization for stability and external validity.\tBuhlmann, Cevid\thttps://arxiv.org/pdf/2008.06234.pdf\n\nA Bayesian nonparametric conditional two-sample test with an application to local causal discovery. \tBoeken, Mooij\thttps://arxiv.org/pdf/2008.07382.pdf\n\nSemiparametric estimation and inference on structural target functions using machine learning and influence functions.\tCurth, Alaa, Schaar, \thttps://arxiv.org/pdf/2008.06461.pdf\n\nEstimating causal effects with the neural autoregressive density estimator. \tGarrido, Borysov, Rich, Pereira\thttps://arxiv.org/pdf/2008.07283.pdf\n\nReparametrization invariance for non-parametric causal discovery.\tJorgensen, Hauberg\thttps://arxiv.org/pdf/2008.05552.pdf\n\nMultivariate counterfactual systems and causal graphical models.\tShpitser, Richardson, Robins\thttps://arxiv.org/pdf/2008.06017.pdf\n\nCausal inference on discrete data.\tBudhathoki\thttps://eda.mmci.uni-saarland.de/pubs/2020/phd-budhathoki.pdf\n\nCRUDS: Counterfactual recourse using disentangled subspaces.\tDowns, Chu, Yacoby, Doshi-Velez, Pan\thttps://finale.seas.harvard.edu/files/finale/files/cruds-_counterfactual_recourse_using_disentangled_subspaces.pdf\n\nA causal lens for peeking into black box predictive models: predictive model interpretation via causal attribution. \tKhademi, Honava\thttps://arxiv.org/pdf/2008.00357.pdf\n\nImproving and assessing causal inference algorithms for DAGs.\tEigenmann\thttps://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/428225/1/DoctoralthesisMarcoEigenmann.pdf\n\nDifferentiable causal discovery from interventional data. \tBrouillard, Lachapelle, Lacoste, Lacoste-Julien, Drouin\thttps://arxiv.org/pdf/2007.01754.pdf\n\nCausal feature selection via orthogonal search. \tRaj, Bauer, Soleymani, Besserve\thttps://arxiv.org/pdf/2007.02938.pdf\n\nHigh-recall causal discovery for autocorrelated time series with latent confounders.\tGerhardus, Runge\thttps://arxiv.org/pdf/2007.01884.pdf\n\nAdapting text embeddings for causal inference. \tVeitch, Sridhar, Blei\thttp://www.auai.org/uai2020/proceedings/376_main_paper.pdf\n\nFlagpoles anyone? Causal and explanatory asymmetries. \tWoodward\thttp://philsci-archive.pitt.edu/17419/1/flagpoles2%20with%20changes%20accepted%207.1.20.pdf\n\nIGNITE: a minimax game toward learning individual treatment effects from networked observational data.  \tGuo, Li, Li, Candan, Raglin, Liu\thttp://www.public.asu.edu/~rguo12/IJCAI20_IGNITE_arxiv.pdf\n\nCounterfactual propagation for semi-supervised individual treatment effect estimation.\tHarada, Kashima\thttps://arxiv.org/pdf/2005.05099.pdf\n\nSimpson's paradox in COVID-19 case fatality rates: a mediation analysis of age-related causal effects.\tvon Kugelgen, Gresele, Scholkopf\thttps://arxiv.org/abs/2005.07180\n\nCounterfactual confounding adjustment for feature representationas learned by deep models: with an application to image classification tasks.\tNeto\thttps://arxiv.org/abs/2004.09466\n\nNecessary and sufficient conditions for causal feature selection in time series with latent common causes. \tMastakouri, Scholkopf, Janzing\thttps://arxiv.org/pdf/2005.08543.pdf\n\nAchieving causal fairness in machine learning.\tWu\thttps://search.proquest.com/openview/5b94283bd4da8edc1b14bff3db4c9e77/1?pq-origsite=gscholar\u0026cbl=18750\u0026diss=y\n\nEfficient intervention design for causal discovery with latents.\tAddanki, Kasiviswanathan, McGregor, Musco\thttps://arxiv.org/pdf/2005.11736.pdf\n\nCausaLM: Causal model explanation through counterfactual language models.\tFeder, Oved, Shalit, Reichart\thttps://arxiv.org/pdf/2005.13407.pdf\n\nBayesian network structure learning with causal effects in the presence of latent variables. \tChobtham, Constantinou\thttps://arxiv.org/pdf/2005.14381.pdf\n\nA principled approach to multiple causal inference.\tMehta\thttps://static1.squarespace.com/static/577d2a80579fb35f94742dbb/t/5eb43f0b8a1cef37852cc5c3/1588870930090/senior_thesis.pdf\n\nStudy causal inference techniques for data-driven personalised decision-making.\tDai\thttps://vrs.amsi.org.au/wp-content/uploads/sites/75/2020/01/dai_zhou_vrs-report.pdf\n\nPhenomenal causality and sensory realism.\tMding, Bruins, Scholkopf, Berens,Wichmann\thttps://journals.sagepub.com/doi/pdf/10.1177/2041669520927038\n\nCausal inference with deep causal graphs. \tParafita, Vitria\thttps://arxiv.org/pdf/2006.08380.pdf\n\nIs independence all your need? On the generalization of representations learned from correlated data.\tTrauble, Creager, Kilbertus, Goyal, Locatello, Scholkopf, Bauer\thttps://arxiv.org/pdf/2006.07886.pdf\n\nLearning decomposed representation for counterfactual inference.\tWu, Kuang, Yuan, Li, Zhou\thttps://arxiv.org/pdf/2006.07040.pdf\n\nRobust recursive partitioning for heterogeneous treatment effects with uncertainty quantification. \tLee, Zhang, Zame, et al.\thttps://arxiv.org/pdf/2006.07917.pdf\n\nLearning causal models online. \tJaved, White, Bengio\thttps://arxiv.org/pdf/2006.07461.pdf\n\nTargeted learning: robust statistics for reproducible research.\tCoyle, Hejazi et al.\thttps://arxiv.org/pdf/2006.07333.pdf\n\nSupervised whole DAG causal discovery.  \tLi, Xiao, Tian\thttps://arxiv.org/pdf/2006.04697.pdf\n\nCausal discovery from incomplete data using an encoder and reinforcement learning\t.  Huang, Zhu, Holloway, Haidar\thttps://arxiv.org/pdf/2006.05554.pdf\n\nIdentifying causal structure in dynamical systems.\tBaumann, Solowjow, Johansson, Trimpe\thttps://arxiv.org/pdf/2006.03906.pdf\n\nOptimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder.\tQi, Hu, Dong, Fan, Dong, Xiao\thttps://www.sciencedirect.com/science/article/abs/pii/S030626192030636X\n\ntvGP-VAE: tensor-variate gaussian process prior variational autoencoder.  \tCampbell, Lio\thttps://arxiv.org/pdf/2006.04788.pdf\n\nOC-FakeDect: classifying deepfakes using one-class variational autoencoder.\tKhalid, Woo \thttp://openaccess.thecvf.com/content_CVPRW_2020/papers/w39/Khalid_OC-FakeDect_Classifying_Deepfakes_Using_One-Class_Variational_Autoencoder_CVPRW_2020_paper.pdf\n\nTuning a variational autoencoder for data accountability problem in the Mars science laboratory ground data system. \tLakhmiri, Alimo, Le Digabel\thttps://arxiv.org/pdf/2006.03962.pdf\n\nCounterfactual vision and language learning. \tAbbasnejad, Teneh, Parvaneh, Shi, Hengel\thttp://openaccess.thecvf.com/content_CVPR_2020/papers/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.pdf\n\nInvariant risk minimization. \tArjovsky, Bottou, Gulrajani, Lopez-Paz\thttps://arxiv.org/pdf/1907.02893.pdf\n\nAmortized causal discovery: learning to infer causal graphs from time-series data.\tLowe, Madras, Zemel, Welling\thttps://arxiv.org/abs/2006.10833\n\nStructural autoencoders improve representations for generation and transfer.\tLeeb, Annadani, Bauer, Scholkopf\thttps://arxiv.org/pdf/2006.07796.pdf\n\nRecurrent independent mechanisms. \tGoyal, Lamb, Hoffmann, Sodhani, Levine, Bengio, Scholkopf\thttps://arxiv.org/abs/1909.10893\n\nA crash course in good and bad controls.\tCinelli, Forney, Pearl\t https://ftp.cs.ucla.edu/pub/stat_ser/r493.pdf\n\nA ladder of causal distances.\tPeyrard, West\thttps://arxiv.org/pdf/2005.02480.pdf\n\nOff-the-shelf deep learning is not enough: parsimony, Bayes and causality\t. Vasudevan, Ziatdinov, Vlcek, Kalinin\thttps://arxiv.org/pdf/2005.01557.pdf\n\nGradient-based neural DAG learning with interventions.\tBrouillard, Drouin, Lachapelle, Lacoste, Lacoste-Julien\thttps://causalrlworkshop.github.io/pdf/CLDM_10.pdf\n\nLearning transferable task schemas by representing causal invariances.\tMadarasz, Behrens\thttps://causalrlworkshop.github.io/pdf/CLDM_25.pdf\n\nDesigning data augmentation for simulating interventions.\tIlse, Tomczak, Forre\thttps://arxiv.org/pdf/2005.01856.pdf \n\nA causal view n robustness of neural networks. \tZhang, Zhang, Li\thttps://arxiv.org/pdf/2005.01095.pdf\n\nEstimation of post-nonlinear causal models using autoencoding structure. \tUemura, Shimizu\thttps://ieeexplore.ieee.org/abstract/document/9053468/\n\nPotential outcome and directed acyclic graph approaches to causality: relevance for empirical practice in economics. Imbens\thttps://arxiv.org/abs/1907.07271\n\nCausal inference analysis with neural networks. \tAlonso\thttps://j1nma.com/documents/JuanManuelAlonso-MTPaper.pdf\n\nA survey on causal inference.\tYao, Chu, Li, Li, Gao, Zhang\thttps://arxiv.org/abs/2002.02770\n\nMissDeepCausal: causal inference from incomplete data using deep latent variable models.\tMayer, Josse, Raimundo, Vert\thttps://arxiv.org/abs/2002.10837\n\nAccurate data-driven prediction does not mean high reproducibility.\tLi, Liu, Le, Liu\thttps://www.nature.com/articles/s42256-019-0140-2?proof=t\n\nCausal models for dynamical systems.\tPeters, Bauer, Pfister\thttps://arxiv.org/pdf/2001.06208.pdf\n\nA critical view of the structural causal model.\tGalanti, Nabati, Wolf\thttps://arxiv.org/abs/2002.10007\n\nTreatment effect estimation with disentangled latent factors\tanon\thttps://arxiv.org/abs/2001.10652\n\nCausalVAE: structured causal disentanglement in variational autoencoder.\tYang, Liu, Chen, Shen, Hao, Wang\thttps://arxiv.org/pdf/2004.08697.pdf\n\nA robust method for estimating individualized treatment effect.\tMeng, Qiao\thttps://arxiv.org/pdf/2004.10108.pdf\n\nDark, beyond deep: a paradigm shift to cognitive AI with humanlike common sense.\tZhu, Gao, Fan, Huang, Edmonds, Liu, Gao, Zhang, Qi, Wu, Tenenbaum, Zhu\thttps://www.sciencedirect.com/science/article/pii/S2095809920300345\n\nMultiMBNN: matched and balanced causal inference with neural networks\tSharma, Gupta, Prasad, Chatterjee, Vig, Shroff\thttps://arxiv.org/abs/2004.13446\n\nFairness by learning orthogonal disentangled representations. \tSarhan, Navab, Eslami, Albarquouni\thttps://arxiv.org/abs/2003.05707\n\nBounding causal effects on continuous outcomes.\tZhang, Bareinboim\thttps://causalai.net/r61.pdf\n\n## 2019\n\nInterpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines.\tNagpal, Wei, Vinzamuri et al\thttps://arxiv.org/abs/1905.03297\n\nDebFace: De-biasing face recognition, \tGong, Liu, Jain\thttps://arxiv.org/abs/1911.08080\n\nLearning individual causal effects from networked observational data. \tGuo, Li, Liu\thttps://arxiv.org/abs/1906.03485\n\nMachine learning in policy evaluation new tools for causal inference\tKreif, DiazOrdaz\thttps://arxiv.org/abs/1903.00402\n\nCounterfactual regression with importance sampling weights.\tHassanpour, Greiner\thttps://www.ijcai.org/Proceedings/2019/0815.pdf\n\nThe causal structure of suppressor variables.\tKim\thttps://journals.sagepub.com/doi/10.3102/1076998619825679\n\nA survey of learning causality with data: problems and methods\tGuo, Cheng, Li, Hahn, Liu\thttps://arxiv.org/abs/1809.09337\n\nPerfect match: a simple method for learning representations for counterfactual inference with neural networks.\tSchwab, Linhardt, Karlen\thttps://arxiv.org/abs/1810.00656\n\nCausality matters in medical imaging. \tCastro, Walker,  Glocker\thttps://arxiv.org/abs/1912.08142\n\nReducing selection bias in counterfactual reasoning for individual treatment effects estimation.\tZhang, Lan, Ding, Wang, Hassanpour, Greiner\thttps://arxiv.org/abs/1912.09040\n\nTwo causal principles for improving visual dialog.\tQi, Niu, Huang, Zhang\thttps://arxiv.org/pdf/1911.10496.pdf\n\nLearning disentangled representations for counterfactual regression.\t   Hassanpour, Greiner\thttps://openreview.net/pdf?id=HkxBJT4YvB\n\nUnderstanding human judgments of causality.\tKazama, Suhara, Bogomolov, Pentland\thttps://arxiv.org/pdf/1912.08998.pdf\n\nGraphical causal models for survey inference\tSchuessler, Selb\thttps://osf.io/preprints/socarxiv/hbg3m/\n\nLearning counterfactual representations for estimating individual dose-response curves.\tSchwab, Linhardt, Bauer, Buhmann, Karlen\thttps://arxiv.org/abs/1902.00981\n\nCausal discovery toolbox: uncover causal relationships in Python.\tKalainathan, Goudet\thttps://arxiv.org/abs/1903.02278\n\nCausal inference and data-fusion in econometrics.\tHunermund, Bareinboim\thttps://arxiv.org/pdf/1912.09104.pdf\n\nAdapting neural networks for the estimation of treatment effects\tShi, Blei, Veitch\thttps://arxiv.org/pdf/1906.02120.pdf\n\nCopy, paste, infer: a robust analysis of twin networks for counterfactual inference.\tGraham, Lee, Perov\thttps://cpb-us-w2.wpmucdn.com/sites.coecis.cornell.edu/dist/a/238/files/2019/12/Id_65_final.pdf\n\n\n## 2018\n\nChallenging the hegemony of randomized controlled trails.\tPearl\t https://www.ncbi.nlm.nih.gov/pubmed/29704961\n\nUnderstanding and misunderstanding randomized controlled trials.\tDeaton, Cartwright\thttps://www.sciencedirect.com/science/article/pii/S0277953617307359\n\nUsing latent variable models to improve causal estimation. \tOktay\thttps://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2241\u0026context=dissertations_2\n\nLearning representations for counterfactual inference.\tJohansson, Shalit, Sontag\thttps://arxiv.org/abs/1605.03661\n\nRepresentation learning for treatment effect estimation from observational data. \tYao, Li, Li, Huai, Gao, Zhang\thttps://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf\n\nGANITE: Estimation of individualized treatment effects using generative adversarial nets.\tYoon, Jordan, van der Schaar\thttps://openreview.net/forum?id=ByKWUeWA-\n\nCausal reasoning for algorithmic fairness.  \tLoftus, Russell, Kusner, Silva\thttps://arxiv.org/pdf/1805.05859.pdf\n\nGranger-causal attentive mixtures of experts: learning important features with neural networks. \tSchwab, Miladinovic, Karlen\thttps://arxiv.org/pdf/1802.02195.pdf\n\nStructural causal bandits: where to intervene?\tLee, Bareinboim\thttps://causalai.net/r36.pdf\n\n\n## 2017\n\nCounterfactual fairness.\tKusner, Loftus, Russell, Silva\thttps://papers.nips.cc/paper/6995-counterfactual-fairness\n\nAvoiding discrimination through causal reasoning.\tKilbertus, Rojas-Carulla, Parascandolo, Hardt, Janzing, Scholkopf\thttps://arxiv.org/abs/1706.02744\n\nElements of causal inference.\tPeters, Janzing, Scholkopf\thttps://mitpress.mit.edu/books/elements-causal-inference\n\nDeep counterfactual networks with propensity-dropout.\tAlaa, Weisz, van der Schaar\thttps://arxiv.org/abs/1706.05966\n\nEstimating individual treatment effect: generalization bounds and algorithms.\tShalit, Johansson, Sontag\thttps://arxiv.org/pdf/1606.03976.pdf\n\nCausal effect inference with deep latent-variable models.   \tLouizos, Shalit, Mooij, Sontag, Zemel, Welling\thttps://arxiv.org/pdf/1705.08821.pdf\n\nWhen worlds collide: integrating different counterfactual assumptions in fairness.\tRussell, Kusner, Loftus, Silva\thttps://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness.pdf\n\n\n\n## 2016\n\nRecursive partitioning for heterogeneous causal effects. \tAthey, Imbens\thttps://www.pnas.org/content/pnas/113/27/7353.full.pdf\n\nDouble/debiased machine learning for treatment and structural parameters.\tChernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins\thttps://economics.mit.edu/files/12538\n\nTargeted maximum likelihood estimation for causal inference in observational studies.\tSchuler, Rose\thttps://academic.oup.com/aje/article/185/1/65/2662306\n\nCausal inference in law: an epidemiological perspective.\tSiegerink, Hollander, Zeegers, Middelburg\thttps://www.cambridge.org/core/journals/european-journal-of-risk-regulation/article/causal-inference-in-law-an-epidemiological-perspective/316A2F893F03FCA836354BCC21BDC1E4\n\nEquality of opportunity in supervised learning. \tHardt, Price, Srebro\thttps://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf\n\n## 2015\n\nDo-calculus when the true graph is unknown.\tHyttinen, Eberhardt, Jarvisalo\n\nExploring causal relationships in visual object tracking.\tLebeda, Hadfield, Bowden\thttp://openaccess.thecvf.com/content_iccv_2015/papers/Lebeda_Exploring_Causal_Relationships_ICCV_2015_paper.pdf\n\nBandits with unobserved confounders: a causal approach. \tBareinboim, Forney, Pearl\thttps://ftp.cs.ucla.edu/pub/stat_ser/r460.pdf\n\n## 2014\n\nEntering the era of data science: targeted learning and the integration of statistics and computational data analysis. \tvan der Laan, Starmans \thttps://www.hindawi.com/journals/as/2014/502678/\n\nSeeing the arrow of time. \tPickup, Pan, Wei, Shih, Zhang, Zisserman, Scholkopf, Freeman\thttps://www.robots.ox.ac.uk/~vgg/publications/2014/Pickup14/pickup14.pdf\n\nCausal diagrams for interference.\tOgburn, VanderWeele\thttps://arxiv.org/pdf/1403.1239.pdf\n\nCausal models and learning from data: integrating causal modeling and statistical estimation. \tPetersen, van der Laan\thttps://journals.lww.com/epidem/Fulltext/2014/05000/Causal_Models_and_Learning_from_Data__Integrating.13.aspx\n\n## 2013\n\nCounterfactual reasoning and learning systems: the example of computational advertising. \tBottou, Peters, Quinonero-Candela, Charles, Chickering, Portugaly, Ray, Simard, Snelson\thttps://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/bottou13a.pdf\n\nA sound and complete algorithm for learning causal models from relational data.\tMaier, Marazopoulou, Arbour, David\thttps://arxiv.org/abs/1309.6843\n\n## 2012\n\nOn a class of bias-amplifying variables that endanger effect estimates.\tPearl\t https://arxiv.org/abs/1203.3503\n\nQuantifying causal influences\tJanzing, Balduzzi, Grosse-Wentrup, Scholkopf\thttps://arxiv.org/pdf/1203.6502.pdf\thttp://webdav.tuebingen.mpg.de/causality/\n\nInformation flows in causal networks. \tAy, Polani\thttps://sfi-edu.s3.amazonaws.com/sfi-edu/production/uploads/sfi-com/dev/uploads/filer/45/5f/455fd460-b6b0-4008-9de1-825a5e2b9523/06-05-014.pdf\n\n\n## 2011\n\nSeparated at birth: statisticians, social scientists, and causality in health services research.\tDowd\thttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3064910/\n\n## 2010\n\nProabilistic latent variable models for distinguishing between cause and effect.\tMooij, Stegle, Janzing, Zhang, Scholkopf\thttps://papers.nips.cc/paper/4173-probabilistic-latent-variable-models-for-distinguishing-between-cause-and-effect.pdf\n\n\n## 2009\n\nIn the light of time.\tTuisku, Pernu, Annila\thttps://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2008.0494\n\n\n## 2005\n\nCausal inference using potential outcomes: design, modeling, decisions.\tRubin\thttps://www.jstor.org/stable/2335942\n\nDoes matching overcome LaLonde's critique of nonexperimental estimators?\tSmith, Todd\thttps://www.sciencedirect.com/science/article/abs/pii/S030440760400082X\n\n\n## 1983\n\nThe central role of the propensity score in observational studies for causal effects.\tRosenbaum, Rubin\thttps://www.jstor.org/stable/2335942\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatthewvowels1%2FAwesome-Causal-Inference","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatthewvowels1%2FAwesome-Causal-Inference","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatthewvowels1%2FAwesome-Causal-Inference/lists"}