Causal Inference for fMRI Time Series Data With Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat

Prof. Michael Sobel

Columbia University

About the Speaker

Michael Sobel is a professor in the statistics department at Columbia University. He has published extensively in the area of social statistics, particularly on structural equation models and categorical data analysis, and is a past editor of Sociological Methodology. His more recent work is in the area of causal inference, where he has published papers on mediation, compliance, interference, and longitudinal data analysis using fixed effects models. His most recent work takes up the subject of making causal inferences for fMRI data.

Abstract

Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate.

Building on Rubin’s causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time series data. In the usual statistical literature on causal inference, potential outcomes, assumed to be measured without systematic error, are used to define unit and average causal effects. However, in general the potential BOLD responses are measured with stimulus dependent systematic error. Thus we define unit and average causal effects that are free of systematic error. In contrast to the usual case of a randomized experiment where adjustment for intermediate outcomes leads to biased estimates of treatment effects, here the failure to adjust for task dependent systematic error leads to biased estimates. We therefore adjust for systematic error using measured “noise covariates,” using a linear mixed model to estimate the effects and the systematic error.

Our results are important for neuroscientists, who typically do not adjust for systematic error. They should also prove useful to researchers in other areas where responses are measured with error and in fields where large amounts of data are collected on relatively few subjects. To illustrate our approach, we reanalyze data from a social evaluative threat task, comparing the findings with results that ignore systematic error.