Online Program Home
My Program

Abstract Details

Activity Number: 592 - Evaluating Impact in Networks: Causal Inference with Interference
Type: Invited
Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #300510
Title: Matching Methods for Networked Causal Inference
Author(s): Alexander Volfovsky*
Companies: Duke University
Keywords: causal inference; networks; interference

A classical problem in causal inference is that of matching treatment units to control units in an observational dataset. This problem is distinct from simple estimation of treatment effects as it provides additional practical interpretability of the underlying causal mechanisms that is not available without matching. Some of the main challenges in developing matching methods arise from the tension among the desire for granular and interpretable matched groups while having enough data to learn causal effects while dealing with complicating factors such as networks and non-independence among units. To deal with the influence of networks we propose to learn which network components are relevant to our causal questions. We do this within the Fast Large-scale Almost Matching Exactly (FLAME) framework which at its core proposes an optimization objective for match quality that captures covariates and structures that are integral for making causal statements while encouraging as many matches as possible. We demonstrate that this framework its extensions to continuous covariates are able to construct good matched groups while accounting for complex network structures.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program