Pinned Repositories
causalTree
Working repository for Causal Tree and extensions
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.
MCPanel
Matrix Completion Methods for Causal Panel Data Models
ML-Causal-Inference
Recreated and adopted code from Susan Athey's lecture on Machine Learning and causal inference
orthoml
Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Semenova, Goldman, Chernozhukov, Taddy (2017) https://arxiv.org/abs/1712.09988
postDoubleR
Double Machine Learning as in Chernozhukov et al. (2018)
rlearner
Quasi-Oracle Estimation of Heterogeneous Treatment Effects
hbj-code's Repositories
hbj-code/MCPanel
Matrix Completion Methods for Causal Panel Data Models
hbj-code/postDoubleR
Double Machine Learning as in Chernozhukov et al. (2018)
hbj-code/causalTree
Working repository for Causal Tree and extensions
hbj-code/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.
hbj-code/ML-Causal-Inference
Recreated and adopted code from Susan Athey's lecture on Machine Learning and causal inference
hbj-code/orthoml
Code associated with paper: Orthogonal Machine Learning for Demand Estimation: High-Dimensional Causal Inference in Dynamic Panels, Semenova, Goldman, Chernozhukov, Taddy (2017) https://arxiv.org/abs/1712.09988
hbj-code/rlearner
Quasi-Oracle Estimation of Heterogeneous Treatment Effects