Treatment Effects Python Packages

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.

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.

rdrobust

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

diff-diff

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

rddensity

Manipulation testing using local polynomial density methods.

causalegm

A General Causal Inference Framework by Encoding Generative Modeling

causeinfer

Machine learning based causal inference/uplift in Python

recon

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

sccausalvi

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

pyuplift

Lightweight uplift modeling framework for Python

rdlocrand

Local Randomization Methods for RD Designs

asbe

Active Learning for treatment effect estimation

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.

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.

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.

rdmulti

Analysis of RD Designs with multiple cutoffs or scores

guangtestbeat

This package contains several methods for calculating Conditional Average Treatment Effects

firstbeatlu

This package contains several methods for calculating Conditional Average Treatment Effects

lzbeat

This package contains several methods for calculating Conditional Average Treatment Effects

rdpower

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

lubeat

This package contains several methods for calculating Conditional Average Treatment Effects

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.