Treatment Effects Collection
Repositories tagged with "treatment-effects"
RARE
TCG-style cards with ATK/DEF/SPD stats
Repositories tagged with "treatment-effects"
dowhy
py-why
โDoWhy 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. โ
EconML
py-why
โALICE (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.โ
causal-ml
jvpoulos
โMust-read papers and resources related to causal inference and machine (deep) learningโ
diff-diff
igerber
โDifference-in-Differences causal inference in Python. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies. sklearn-like API, validated against R.โ
StatsPAI
brycewang-stanford
โStatsPAI is the first agent-native Python library for causal inference and applied econometrics โ unified API, broad cross-method coverage, structured result objects, machine-readable schemas, an MCP server, and R/Stata parity validation.โ
CATENets
AliciaCurth
โSklearn-style implementations of Neural Network-based Conditional Average Treatment Effect (CATE) Estimators. โ
DESCN
kailiang-zhong
โImplementation of paper DESCN, which is accepted in SIGKDD 2022.โ
rdrobust
rdpackages
โRobust Local Polynomial Methods for RD Designsโ
OpenASCE
Open-All-Scale-Causal-Engine
โOpenASCE (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.โ
network-deconfounder-wsdm20
rguo12
โCode for the WSDM '20 paper, Learning Individual Causal Effects from Networked Observational Data.โ
CausalEGM
SUwonglab
โA General Causal Inference Framework by Encoding Generative Modelingโ
causeinfer
andrewtavis
โMachine learning based causal inference/uplift in Pythonโ