1 citations · 1 across the 5 of their papers we have counts for
7 papers
Sensitivity of weighted least squares estimators to omitted variables
Leonard Wainstein, Chad Hazlett
We introduce tools for assessing the sensitivity, to unobserved confounding, of a common estimator of causal effects that employs weights: the weighted linear regression of the out…
Inference with weights: Residualization produces short, valid intervals for varying estimands and varying resampling processes
Erin Hartman, Chad Hazlett, Arisa Sadeghpour
Weighting procedures are used in observational causal inference to adjust for covariate imbalance within the sample. Common practice for inference is to estimate robust standard er…
Post-treatment problems: What can we say about the effect of a treatment among sub-groups who (would) respond in some way?
Chad Hazlett, Nina McMurry, Tanvi Shinkre
Investigators are often interested in how a treatment affects an outcome for units responding to treatment in a certain way. We may wish to know the effect among units that, for ex…
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation
Soonhong Cho, Doeun Kim, Chad Hazlett
Many inferential tasks involve fitting models to observed data and predicting outcomes at new covariate values, requiring interpolation or extrapolation. Conventional methods selec…
Demystifying and avoiding the OLS "weighting problem": Unmodeled heterogeneity and straightforward solutions
Tanvi Shinkre, Chad Hazlett
Researchers frequently estimate treatment effects by regressing outcomes (Y) on treatment (D) and covariates (X). Even without unobserved confounding, the coefficient on D yields a…
Causal progress with imperfect placebo treatments and outcomes
Adam Rohde, Chad Hazlett
In the quest to make defensible causal claims from observational data, it is sometimes possible to leverage information from "placebo treatments" and "placebo outcomes". Existing a…
Kernel Balancing: A flexible non-parametric weighting procedure for estimating causal effects
Chad Hazlett
In the absence of unobserved confounders, matching and weighting methods are widely used to estimate causal quantities including the Average Treatment Effect on the Treated (ATT).…