mhtopt R / Stata / Python
A package for economically optimal multiple-testing adjustments.
Software
Open-source implementations of methods developed in my research. These packages were developed in collaborations with Haya Alsharif, Jake Carlson, Erick Lopez, Sasha Ostasovskyi, Paul Niehaus, Kaspar Wuthrich, Ashesh Rambachan, Rahul Singh, Zhaonan Qu, Xuelin Yang, and Justin Waddy.
Inference
remoteoutcome R
An R package for program evaluation with remotely sensed outcomes.
Policy targeting
NetworkTargeting R
An R package for estimating whom to treat when outcomes may exhibit spillover effects.
FairTargeting R
An R package for welfare-improving policy learning under fairness and Pareto-efficiency considerations.
Experimental design
CausalClustering R
An R package for designing cluster-randomized experiments under network interference.
robustExpDesign R
An R package for choosing informative experiments when experimental evidence is combined with potentially biased external evidence.
Panel data
TROP estimator Python / Stata
Implementations of the Triply Robust Panel estimator for treatment effects in panel data.
DynBalancing R
An R package for inference on the effects of time-varying treatments with high-dimensional covariates.