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Treatment Effects Python Packages

Python packages with the GitHub topic treatment-effects. Sorted by relevance, with stars and monthly downloads.
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.

606K 5K 806
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.

144K 8K 1K
rdpackages
rdrobust

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

123K 91 42
igerber
diff-diff

Difference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.

57K 167 25
rdpackages
rddensity

Manipulation testing using local polynomial density methods.

2K 12 9
SUwonglab
causalegm

A General Causal Inference Framework by Encoding Generative Modeling

1K 74 11
andrewtavis
causeinfer

Machine learning based causal inference/uplift in Python

678 63 12
cantinilab
recon

Exploring multicellular coordination from single-cell gene expression / multi-omics using mutlilayer network representations

575 8 0
ShaokunAn
sccausalvi

Perturbational analysis by causality-aware generative model for single-cell RNA-sequencing data

539 21 3
duketemon
pyuplift

Lightweight uplift modeling framework for Python

393 30 3
rdpackages
rdlocrand

Local Randomization Methods for RD Designs

363 8 10
puhazoli
asbe

Active Learning for treatment effect estimation

306 1 0
Microsoft
beat-ml1

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.

158 5K 806
Microsoft
beat-test

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.

156 5K 806
Microsoft
beataalu

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.

151 5K 806
rdpackages
rdmulti

Analysis of RD Designs with multiple cutoffs or scores

136 4 9
Microsoft
guangtestbeat

This package contains several methods for calculating Conditional Average Treatment Effects

114 5K 806
Microsoft
firstbeatlu

This package contains several methods for calculating Conditional Average Treatment Effects

112 5K 806
Microsoft
lzbeat

This package contains several methods for calculating Conditional Average Treatment Effects

110 5K 806
rdpackages
rdpower

Power and sample size calculations for RD designs using robust bias-corrected local polynomial inference.

109 4 3
Microsoft
lubeat

This package contains several methods for calculating Conditional Average Treatment Effects

106 5K 806
Open-All-Scale-Causal-Engine
openasce

OpenASCE (Open All-Scale Casual Engine) is a Python package for end-to-end large-scale causal learning. It provides causal discovery, causal effect estimation and attribution algorithms all in one package.

70 81 10
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