Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding

02/18/2021
by   Jiajing Zheng, et al.
0

Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. On the one hand, a latent variable model fit to the observed treatments can identify essential aspects of the distribution of unobserved confounders. On the other hand, it has been shown that even when the latent confounder distribution is known exactly, causal effects are still not point identifiable. Thus, the practical benefits of latent variable modeling in multi-treatment settings remain unclear. We clarify these issues with a sensitivity analysis method that can be used to characterize the range of causal effects that are compatible with the observed data. Our method is based on a copula factorization of the joint distribution of outcomes, treatments, and confounders, and can be layered on top of arbitrary observed data models. We propose a practical implementation of this approach making use of the Gaussian copula, and establish conditions under which causal effects can be bounded. We also describe approaches for reasoning about effects, including calibrating sensitivity parameters, quantifying robustness of effect estimates, and selecting models which are most consistent with prior hypotheses.

READ FULL TEXT

page 1

page 2

page 3

page 4

07/31/2019

Multi-cause causal inference with unmeasured confounding and binary outcome

Unobserved confounding presents a major threat to causal inference in ob...
08/13/2022

Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes

We propose an approach for assessing sensitivity to unobserved confoundi...
11/15/2021

Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding

In causal estimation problems, the parameter of interest is often only p...
12/11/2020

A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome

In the absence of a randomized experiment, a key assumption for drawing ...
05/18/2020

Is being an only child harmful to psychological health?: Evidence from an instrumental variable analysis of China's One-Child Policy

This paper evaluates the effects of being an only child in a family on p...
05/18/2019

Causal Inference for Multiple Non-Randomized Treatments using Fractional Factorial Designs

When interest lies in assessing the effect of multiple treatments on an ...

Code Repositories