Get Started
Research questionHow can extreme quantile treatment effects be estimated under heavy tails while respecting location invariance?Tail treatment effects may concern quantiles beyond the observed data range, making estimation sensitive to assumptions about extreme-value behavior. An estimator should also preserve causal contrasts when both potential-outcome distributions undergo the same location shift.
Economics
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.A Location-Invariant Estimator of Extremal Quantile Treatment Effects for Heavy-Tailed DistributionsThe source develops an inverse-propensity-weighted extremal QTE estimator with asymptotic theory, variance estimation, and simulation-based checks of invariance, threshold behavior, and interval coverage. The evidence is theoretical and simulated rather than an application to observed treatment data.research paper · Sep 4, 2026
Related questions
How can sequential treatment allocation learn unknown outcome variances while preserving efficient ATE inference?How can we recover a target site's full treated-outcome distribution from controls alone under cross-site heterogeneity?How can we estimate counterfactual outcomes with valid uncertainty as treatment patterns shift?How can we reliably select a heterogeneous treatment effect estimator without observing treatment effects?
Home
Topics
Search
Library