Work

Estimation of Personalized Effects Associated With Causal Pathways

Year: 2018

Type: conference-paper

Abstract: , in settings such as personalized medicine, the goal is to map a unit's characteristics to a treatment tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic treatment regime literature. In healthcare settings, optimizing policies with respect to a particular causal pathway is often of interest as well. In the context of average treatment effects, estimation of effects associated with causal pathways is considered in the mediation analysis literature. In this paper, we combine mediation analysis and dynamic treatment regime ideas and consider how unit characteristics may be used to tailor a treatment strategy that maximizes an effect along specified sets of causal pathways. In particular, we define counterfactual responses to such policies, give a general identification algorithm for these counterfactuals, and prove completeness of the algorithm for unrestricted policies. A corollary of our results is that the identification algorithm for responses to policies given in [16] is complete for arbitrary policies.

Source: PubMed

DOI: 10.48550/arxiv.1809.10791

Authors: Nabi, Razieh, Kanki, Phyllis, Shpitser, Ilya

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Allergy and Infectious Diseases

Awards: Identification and Inference for Longitudinal Causal Mediation Analysis in HIV, Next Generation Missing Data Methods in HIV Research