DeepAI
Log In Sign Up

Controlling for Unmeasured Confounding in the Presence of Time: Instrumental Variable for Trend

11/06/2020
by   Ting Ye, et al.
0

Unmeasured confounding is a key threat to reliable causal inference based on observational studies. We propose a new method called instrumental variable for trend that explicitly leverages exogenous randomness in the exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasured confounding. Specifically, we use an instrumental variable for trend, a variable that (i) is associated with trend in exposure; (ii) is independent of the potential trends in exposure, potential trends in outcome and individual treatment effect; and (iii) has no direct effect on the trend in outcome and does not modify the individual treatment effect. We develop the identification assumptions using the potential outcomes framework and we propose two measures of weak identification. In addition, we present a Wald estimator and a class of multiply robust and efficient semiparametric estimators, with provable consistency and asymptotic normality. Furthermore, we propose a two-sample summary-data Wald estimator to facilitate investigations of delayed treatment effect. We demonstrate our results in simulated and real datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

04/08/2022

Bespoke Instrumental Variables for Causal Inference

Many proposals for the identification of causal effects in the presence ...
09/21/2022

Structural mean models for instrumented difference-in-differences

In the standard difference-in-differences research design, the parallel ...
12/22/2020

A spectral adjustment for spatial confounding

Adjusting for an unmeasured confounder is generally an intractable probl...
10/27/2022

A Double Machine Learning Trend Model for Citizen Science Data

1. Citizen and community-science (CS) datasets have great potential for ...
08/17/2021

Causal Inference with Noncompliance and Unknown Interference

In this paper, we investigate a treatment effect model in which individu...
11/10/2014

Bounding the Probability of Causation in Mediation Analysis

Given empirical evidence for the dependence of an outcome variable on an...