Non-parametric causal effects based on longitudinal modified treatment policies

06/02/2020
by   Iván Díaz, et al.
0

Most causal inference methods consider counterfactual variables under interventions that set the treatment deterministically. With continuous or multi-valued treatments or exposures, such counterfactuals may be of little practical interest because no feasible intervention can be implemented that would bring them about. Furthermore, violations to the positivity assumption, necessary for identification, are exacerbated with continuous and multi-valued treatments and deterministic interventions. In this paper we propose longitudinal modified treatment policies (LMTPs) as a non-parametric alternative. LMTPs can be designed to guarantee positivity, and yield effects of immediate practical relevance with an interpretation that is familiar to regular users of linear regression adjustment. We study the identification of the LMTP parameter, study properties of the statistical estimand such as the efficient influence function, and propose four different estimators. Two of our estimators are efficient, and one is sequentially doubly robust in the sense that it is consistent if, for each time point, either an outcome regression or a treatment mechanism is consistently estimated. We perform a simulation study to illustrate the properties of the estimators, and present the results of our motivating study on hypoxemia and mortality in Intensive Care Unit (ICU) patients. Software implementing our methods is provided in the form of the open source R package lmtp freely available on GitHub (<https://github.com/nt-williams/lmtp>).

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/19/2023

Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures

This tutorial discusses a recently developed methodology for causal infe...
research
12/20/2019

Non-parametric efficient causal mediation with intermediate confounders

Interventional effects for mediation analysis were proposed as a solutio...
research
05/16/2022

Causal influence, causal effects, and path analysis in the presence of intermediate confounding

Recent approaches to causal inference have focused on the identification...
research
03/28/2022

Efficient and flexible causal mediation with time-varying mediators, treatments, and confounders

Interventional effects have been proposed as a solution to the unidentif...
research
02/07/2022

Causal survival analysis under competing risks using longitudinal modified treatment policies

Longitudinal modified treatment policies (LMTP) have been recently devel...
research
03/15/2023

lmw: Linear Model Weights for Causal Inference

The linear regression model is widely used in the biomedical and social ...
research
12/01/2021

Intervention treatment distributions that depend on the observed treatment process and model double robustness in causal survival analysis

The generalized g-formula can be used to estimate the probability of sur...

Please sign up or login with your details

Forgot password? Click here to reset