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akm-rmst

Adjusted restricted mean survival times in observational studies

cates

Machine Learning Estimation of Heterogeneous Causal Effects

causal-ml

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

causaltoolbox

cfrnet

Counterfactual Regression

coprimary_crt

coursera-ng-neural-networks-and-deep-learning

Build logistic regression, neural network models for classification

double-rouble-representation-learning

Codebase for "Double Robust Representation Learning for Counterfactual Prediction"

drnet

💉📈 Dose response networks (DRNets) are a method for learning to estimate individual dose-response curves for multiple parametric treatments from observational data using neural networks.

duke-mlws-2019

Duke Machine Learning Winter School 2019

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.

hte_comparison

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases

lstmcell

Implement modern LSTM cell by tensorflow and test them by language modeling task for PTB. Highway State Gating, Hypernets, Recurrent Highway, Attention, Layer norm, Recurrent dropout, Variational dropout.

myperfectmatch

ow_sga

ow_sga_survival

owrct_codes_package

Codebase for OW-RCT paper

perfect_match

➕➕ Perfect Match is a simple method for learning representations for counterfactual inference with neural networks.

planets

plusdatascience

Materials for +DataScience In-Person Learning Experiences (IPLEs)

psweight

psweight.sga

Performs propensity score weighting for causal subgroup analysis of observational studies and randomized trials. When the covariates and subgrouping variables are provided, this package allows to automatically perform the Post-LASSO to select important covariate-subgroup interactions and generate the Connect-S plot, introduced in Yang et al. (2021) <https://doi.org/10.1002/sim.9029 >

psweight.sga0

Performs propensity score weighting for causal subgroup analysis of observational studies and randomized trials. When the covariates and subgrouping variables are provided, this package allows to automatically perform the Post-LASSO to select important covariate-subgroup interactions and generate the Connect-S plot, introduced in Yang et al. (2021) <https://doi.org/10.1002/sim.9029 >

python-hm

rmsn_nips_2018

sga_rct

time_varying_matching

The codebase for 'matching with time-dependent treatments: A review and look forward' by Thomas et al.