Assessing Impact of Unobserved Confounders with Sensitivity Index Probabilities through Pseudo-Experiments

08/26/2020
by   Beilin Jia, et al.
0

Unobserved confounders are a long-standing issue in causal inference using propensity score methods. This study proposed nonparametric indices to quantify the impact of unobserved confounders through pseudo-experiments with an application to real-world data. The study finding suggests that the proposed indices can reflect the true impact of confounders. It is hoped that this study will lead to further discussion on this important issue and help move the science of causal inference forward.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/15/2022

ρ-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders

We propose a new sensitivity analysis model that combines copulas and no...
research
08/02/2023

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

Causal inference plays a vital role in diverse domains like epidemiology...
research
04/09/2018

Merging joint distributions via causal model classes with low VC dimension

If X,Y,Z denote sets of random variables, two different data sources may...
research
06/19/2020

Relaxing monotonicity in endogenous selection models and application to surveys

This paper considers endogenous selection models, in particular nonparam...
research
06/17/2022

On the probability of invalidating a causal inference due to limited external validity

External validity is often questionable in empirical research, especiall...
research
02/02/2022

Ergodic descriptors of nonergodic stochastic processes

The stochastic processes underlying the growth and stability of biologic...
research
08/16/2022

Collaborative causal inference on distributed data

The development of technologies for causal inference with the privacy pr...

Please sign up or login with your details

Forgot password? Click here to reset