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Found 7 papers, 0 papers with code
Covariate Balancing and the Equivalence of Weighting and Doubly Robust Estimators of Average Treatment Effects
We show that when the propensity score is estimated using a suitable covariate balancing procedure, the commonly used inverse probability weighting (IPW) estimator, augmented inverse probability weighting (AIPW) with linear conditional mean, and inverse probability weighted regression adjustment (IPWRA) with linear conditional mean are all numerically the same for estimating the average treatment effect (ATE) or the average treatment effect on the treated (ATT).
Another Look at the Linear Probability Model and Nonlinear Index Models
We reassess the use of linear models to approximate response probabilities of binary outcomes, focusing on average partial effects (APE).
A Design-Based Approach to Spatial Correlation
When observing spatial data, what standard errors should we report?
Doubly Robust Estimation of Local Average Treatment Effects Using Inverse Probability Weighted Regression Adjustment
We also propose a DR version of the Hausman test that can be used to assess the unconfoundedness assumption through a comparison of different estimates of the average treatment effect on the treated (ATT) under one-sided noncompliance.
Abadie's Kappa and Weighting Estimators of the Local Average Treatment Effect
Several other estimators, which are unnormalized, do not satisfy the properties of scale invariance with respect to the natural logarithm and translation invariance, thereby exhibiting sensitivity to the units of measurement when estimating the LATE in logs and the centering of the outcome variable more generally.
Simple Alternatives to the Common Correlated Effects Model
We study estimation of factor models in a fixed-T panel data setting and significantly relax the common correlated effects (CCE) assumptions pioneered by Pesaran (2006) and used in dozens of papers since.
Sampling-based vs. Design-based Uncertainty in Regression Analysis
We derive standard errors that account for design-based uncertainty instead of, or in addition to, sampling-based uncertainty.
Statistics Theory Econometrics Statistics Theory
