7 papers
Estimating Representative Causal Effects with Double Machine Learning
Apoorva Lal, Winston Chou
Double Machine Learning is widely used to estimate treatment effects from non-experimental data. The "residuals-on-residuals" regression (RORR) is especially popular for its simpli…
AI-Assisted Variance Reduction in Randomized Experiments
David Arbour, Eli Ben-Michael, Avi Feller +2
Generative AI and large language models can produce realistic predictions of human behavior from rich, unstructured inputs with little to no task-specific training data. Recent wor…
Long-Term Causal Inference with Many Noisy Proxies
Apoorva Lal, Guido Imbens, Peter Hull
We propose a method for estimating long-term treatment effects with many short-term proxy outcomes: a central challenge when experimenting on digital platforms. We formalize this c…
When can we get away with using the two-way fixed effects regression?
Apoorva Lal
The use of the two-way fixed effects regression in empirical social science was historically motivated by folk wisdom that it uncovers the Average Treatment effect on the Treated (…
Does Regression Produce Representative Causal Rankings?
Apoorva Lal
We examine the challenges in ranking multiple treatments based on their estimated effects when using linear regression or its popular double-machine-learning variant, the Partially…
Large Scale Longitudinal Experiments: Estimation and Inference
Apoorva Lal, Alexander Fischer, Matthew Wardrop
Large-scale randomized experiments are seldom analyzed using panel regression methods because of computational challenges arising from the presence of millions of nuisance paramete…