2 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
The Multiplicative Quasi-Instrumental Variable Model
Jiewen Liu, Chan Park, David Richardson +1
We introduce the Multiplicative Quasi-Instrumental Variable (MQIV) model, a framework for causal inference with unmeasured confounding that leverages an instrument that may be impe…
Single Proxy Control
Chan Park, David Richardson, Eric Tchetgen Tchetgen
Negative control variables are sometimes used in non-experimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome th…
Universal Difference-in-Differences for Causal Inference in Epidemiology
Eric Tchetgen Tchetgen, Chan Park, David Richardson
Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. Th…
Alternative approaches for analysing repeated measures data that are missing not at random
Oliver Dukes, David Richardson, Eric Tchetgen Tchetgen
We consider studies where multiple measures on an outcome variable are collected over time, but some subjects drop out before the end of follow up. Analyses of such data often proc…
Structural Nested Mean Models Under Parallel Trends Assumptions
Zach Shahn, Oliver Dukes, Meghana Shamsunder +3
We link and extend two approaches to estimating time-varying treatment effects on repeated continuous outcomes--time-varying Difference in Differences (DiD; see Roth et al. (2023)…
Using negative controls to identify causal effects with invalid instrumental variables
Oliver Dukes, David B. Richardson, Zachary Shahn +2
Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. I…