A nonparametrically efficient estimator for network quantile causal effects under partial interference achieves parametric convergence rates via three-way cross-fitting and flexible nuisance estimation.
Pearl J (2003)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.
citing papers explorer
-
Nonparametric efficient inference for network quantile causal effects under partial interference
A nonparametrically efficient estimator for network quantile causal effects under partial interference achieves parametric convergence rates via three-way cross-fitting and flexible nuisance estimation.
-
Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation
Large-scale study finds that counterfactual metrics on semi-simulated data do not select the same estimators as observable metrics on real data, and benchmark rankings fail to transfer.
-
A Review of Causal Decision Making
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.