2 papers
econ.EM2026
Reevaluating Causal Estimation Methods with Data from a Product Release
Justin Young, Eleanor Wiske Dillon
Recent developments in causal machine learning methods have made it easier to estimate flexible relationships between confounders, treatments and outcomes, making unconfoundedness…
stat.ML2025
A Meta-learner for Heterogeneous Effects in Difference-in-Differences
Hui Lan, Haoge Chang, Eleanor Dillon +1
We address the problem of estimating heterogeneous treatment effects in panel data, adopting the popular Difference-in-Differences (DiD) framework under the conditional parallel tr…