A Semiparametric Approach to Model-based Sensitivity Analysis in Observational Studies

10/30/2019
by   Bo Zhang, et al.
0

When drawing causal inference from observational data, there is always concern about unmeasured confounding. One way to tackle this is to conduct a sensitivity analysis. One widely-used sensitivity analysis framework hypothesizes the existence of a scalar unmeasured confounder U and asks how the causal conclusion would change were U measured and included in the primary analysis. Works along this line often make various parametric assumptions on U, for the sake of mathematical and computational simplicity. In this article, we substantively further this line of research by developing a valid sensitivity analysis that leaves the distribution of U unrestricted. Our semiparametric estimator has three desirable features compared to many existing methods in the literature. First, our method allows for a larger and more flexible family of models, and mitigates observable implications (Franks et al., 2019). Second, our methods work seamlessly with any primary analysis that models the outcome regression parametrically. Third, our method is easy to use and interpret. We construct both pointwise confidence intervals and confidence bands that are uniformly valid over a given sensitivity parameter space, thus formally accounting for unknown sensitivity parameters. We apply our proposed method on an influential yet controversial study of the causal relationship between war experiences and political activeness using observational data from Uganda.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/30/2023

Sensitivity Analysis for Causal Effects in Observational Studies with Multivalued Treatments

One of the fundamental challenges in drawing causal inferences from obse...
research
01/29/2023

Sensitivity Analysis of Causal Treatment Effect Estimation for Clustered Observational Data with Unmeasured Confounding

Identifying causal treatment (or exposure) effects in observational stud...
research
04/16/2021

Semiparametric Sensitivity Analysis: Unmeasured Confounding In Observational Studies

Establishing cause-effect relationships from observational data often re...
research
12/30/2022

Sensitivity Analysis with the R^2-calculus

Causal inference necessarily relies upon untestable assumptions; hence, ...
research
10/18/2022

Heteroscedasticity-aware sample trimming for causal inference

A popular method for variance reduction in observational causal inferenc...
research
02/20/2021

Designing Experiments Informed by Observational Studies

The increasing availability of passively observed data has yielded a gro...
research
04/18/2019

The uniform general signed rank test and its design sensitivity

A sensitivity analysis in an observational study tests whether the quali...

Please sign up or login with your details

Forgot password? Click here to reset