5 citations · 5 across the 4 of their papers we have counts for
4 papers
Differentially Private Estimation of Heterogeneous Causal Effects
Fengshi Niu, Harsha Nori, Brian Quistorff +3
Estimating heterogeneous treatment effects in domains such as healthcare or social science often involves sensitive data where protecting privacy is important. We introduce a gener…
Machine Learning for Experimental Design: Methods for Improved Blocking
Brian Quistorff, Gentry Johnson
Restricting randomization in the design of experiments (e.g., using blocking/stratification, pair-wise matching, or rerandomization) can improve the treatment-control balance on im…
Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions
Gentry Johnson, Brian Quistorff, Matt Goldman
When pre-processing observational data via matching, we seek to approximate each unit with maximally similar peers that had an alternative treatment status--essentially replicating…
Pricing Engine: Estimating Causal Impacts in Real World Business Settings
Matt Goldman, Brian Quistorff
We introduce the Pricing Engine package to enable the use of Double ML estimation techniques in general panel data settings. Customization allows the user to specify first-stage mo…