/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

Primary LanguageR

orthoml

Code associated with paper: "Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence" Semenova, Goldman, Chernozhukov, Taddy (2022), Quantitative Economics, forthcoming

Introduction

Code

To replicate Figure 1, please run Figure1Code.R in the main folder.

To replicate Figure 2, one should execute the two files below in the following order

  1. estimate_first_stage_Snacks.R

  2. estimate_second_stage_Snacks.R

We see that Lasso estimates are most concenrated (shrinked towards homogenous specification), Orthogonal Least Squares is most dispersed (and least precise), and Double Orthogonal Ridge is in the middle.

References:

"Double/Debiased Machine Learning for Treatment and Causal Parameters" (Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins), 2017, https://arxiv.org/abs/1608.00060

"Estimation and Inference about Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels" Vira Semenova, Matt Goldman, Victor Chernozhukov, Matt Taddy, 2017, https://economics.mit.edu/files/15984

"Pricing Engine: Estimating Causal Impacts in Real World Business Settings" Matt Goldman, Brian Quistorff, 2018, https://arxiv.org/abs/1806.03285