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Found 4 papers, 1 papers with code
Machine Learning for Variance Reduction in Online Experiments
We consider the problem of variance reduction in randomized controlled trials, through the use of covariates correlated with the outcome but independent of the treatment.
Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions
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 a randomized block design.
Pricing Engine: Estimating Causal Impacts in Real World Business Settings
We introduce the Pricing Engine package to enable the use of Double ML estimation techniques in general panel data settings.
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence
The first step of our procedure is orthogonalization, where we partial out the controls and unit effects from the outcome and the base treatment and take the cross-fitted residuals.
