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https://github.com/soda-inria/caussim
Simulations for predictive model selection in causal inference
https://github.com/soda-inria/caussim
Last synced: about 2 months ago
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Simulations for predictive model selection in causal inference
- Host: GitHub
- URL: https://github.com/soda-inria/caussim
- Owner: soda-inria
- License: bsd-3-clause
- Created: 2023-01-05T07:52:12.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-15T18:34:53.000Z (3 months ago)
- Last Synced: 2024-09-16T18:03:51.100Z (3 months ago)
- Language: Python
- Size: 27 MB
- Stars: 13
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
How to select predictive model for causal inference
===================================================
Overview
--------
This package contains simulations for causal inference, estimators for ATE and
CATE as well as code for experiments described in the paper : How to select
predictive models for causal inference ?
Package Features
----------------
The package code is contained in: [caussim](caussim/)
- `estimation` contains CATE and ATE estimators usable with any scikit-learn compatible base estimators and meta-learners such as TLearner, SLearner or RLearner.
- `simulations` simulations with basis expansion (available Nystroem, Splines)
- `experiences` used to run extensive evaluation of causal metrics on ACIC 2016
and handcrafted simulations.
- `reports` contains the scripts used to derive figures and tables presented in
the paper. The main results are obtained by launching the
- `utils.py` plot utils
- `pdistances` naive implementation of MMD, Total Varation and Jensen Shannon Divergences used to measure population overlap
- `demos` contains notebooks used to create toy example and risks maps for the 2D simulations.
- `data` contains utils to load semi-simulated datasets (ACIC 2016, ACIC 2018,
TWINS). A dedicated [README](data/README.md) is
available in the root data folder.
Experiences
----------
Experiences outputs are mainly csvs (one for each sampled dataset). To launch an experience, run `python scripts/experiences/` and it should output the csv in a dedidacted folder in the corresponding subfolder `data/experiences//`.
**🔎 Replicate the main experience of the paper (section 5.)**, launch the script
[scripts/experiences/causal_scores_evaluation.py](scripts/experiences/causal_scores_evaluation.py).
Make sure that the configurations for the datasets at the beginning of the file
is :
```python
from caussim.experiences.base_config import DATASET_GRID_FULL_EXPES
DATASET_GRID = DATASET_GRID_FULL_EXPES
```
📢 Note that the results of the section 5 are already provided in the zenodo link [`experiences.zip`]([data/experiences.zip](https://zenodo.org/records/13765465?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6ImJmNTFlOWNjLTUxOTYtNGFjNS04YjVjLTIyZWFjMmNhZjQyMyIsImRhdGEiOnt9LCJyYW5kb20iOiJlOTZjZGE4ZmQzNDFkMWUxNTJhYzI0YWI1ZjUxNGViMyJ9.vPuJgBw0A0w02InS9ovWRShKUGTDk4w6k2uwYBZklRiC-p7hlVvZOOyvpg6wsJ6T5MBW30vUCsL_UdBSCmmFMw)).
Reports
-------
Reports outputs are mainly figures for the papers. To obtain the results, run `pytest scripts/reports/` and it should output the figures in one or several corresponding folders in `figures/`.
The main report type is a pytest function contained in the `reports/causal_scores_evaluation.py` script. For each macro-dataset, it plot the results of running a given set of candidate estimators with a fixed nuisance estimator on several generation process of the macro-dataset (often hundreds of sampled datasets).
**🔎 Replicate the main figure of the paper (Figure 3.)**, launch the script
[scripts/reports/_1_r_risk_domination.py](scripts/reports/_1_r_risk_domination.py).
It should take some time because of the high number of simulations results. Make
sure that the appropriate experiences results exists. The one used in the paper
are provided in [`experiences.zip`](data/experiences.zip).
```
pytest scripts/reports/causal_scores_evaluation.py
```
Installation
============
- We recommend the use of poetry and python>=3.9 to manage dependencies.
You can install caussim via
[poetry](https://python-poetry.org/):
```shell script
poetry install
```
or
[pip](https://pip.pypa.io/). In this case you also need to install the dependies listed in the `pyproject.toml`:
```shell script
pip install caussim
```
Dependencies:
------------
python = ">=3.9, <3.11"
python-dotenv = "^0.15.0"
click = "^8.0.1"
yapf = "^0.31.0"
matplotlib = "^3.4.2"
numpy = "^1.20.3"
seaborn = "^0.11.1"
jupytext = "^1.11.5"
rope = "^0.19.0"
scikit-learn = "^1.0"
jedi = "^0.18.0"
tqdm = "^4.62.3"
tabulate = "^0.8.9"
statsmodels = "^0.13.1"
pyarrow = "^6.0.1"
submitit = "^1.4.1"
rpy2 = "^3.4.5"
moepy = "^1.1.4"