Projects in Awesome Lists tagged with treatment-effects

https://github.com/microsoft/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

bayesian-networks causal-inference causal-machine-learning causal-models causality data-science do-calculus graphical-models machine-learning python3 treatment-effects

Last synced: 28 Aug 2024

https://github.com/py-why/dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

bayesian-networks causal-inference causal-machine-learning causal-models causality data-science do-calculus graphical-models machine-learning python3 treatment-effects

Last synced: 01 Oct 2024

https://github.com/microsoft/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

causal-inference causality econometrics economics machine-learning treatment-effects

Last synced: 31 Jul 2024

https://github.com/py-why/EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

causal-inference causality econometrics economics machine-learning treatment-effects

Last synced: 31 Jul 2024

https://github.com/py-why/econml

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

causal-inference causality econometrics economics machine-learning treatment-effects

Last synced: 30 Sep 2024

https://github.com/jvpoulos/causal-ml

Must-read papers and resources related to causal inference and machine (deep) learning

causal-discovery causal-inference causal-learning causal-models counterfactual counterfactual-learning deep-learning estimating-treatment-effects heterogeneous-treatment-effects machine-learning paper-list randomized-controlled-trials representation-learning treatment-effects

Last synced: 31 Jul 2024

https://github.com/rdpackages/rdrobust

Statistical inference and graphical procedures for RD designs using local polynomial and partitioning regression methods.

causal-inference program-evaluation regression-discontinuity-designs treatment-effects

Last synced: 02 Aug 2024

https://github.com/rguo12/CIKM18-LCVA

Code for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.

causal-inference network-embedding treatment-effects variational-autoencoder variational-inference

Last synced: 01 Aug 2024