Nonparametric causal inference for optogenetics: sequential excursion effects for dynamic regimes

TL;DR

This paper introduces a nonparametric causal inference framework for optogenetics experiments, enabling analysis of complex treatment sequences and overcoming limitations of standard methods to answer richer scientific questions.

Contribution

It extends causal inference methods to dynamic regimes in optogenetics, including new models and estimators that handle positivity violations and provide richer causal effect insights.

Findings

Proposed a nonparametric framework for open-loop optogenetics analysis.

Extended marginal structural models to closed-loop designs with positivity issues.

Applied the method to neuroscience data, revealing causal effects obscured by standard analyses.

Abstract

Optogenetics is a powerful neuroscience technique for studying how neural circuit manipulation affects behavior. Standard analysis conventions discard information and severely limit the scope of the causal questions that can be probed. To address this gap, we 1) draw connections to the causal inference literature on sequentially randomized experiments, 2) propose a non-parametric framework for analyzing "open-loop" (static regime) optogenetics behavioral experiments, 3) derive extensions of history-restricted marginal structural models for dynamic treatment regimes with positivity violations for "closed-loop" designs, and 4) propose a taxonomy of identifiable causal effects that encompass a far richer collection of scientific questions compared to standard methods. From another view, our work extends "excursion effect" methods, popularized recently in the mobile health literature, to enable estimation of causal contrasts for treatment sequences in the presence of positivity violations. We describe sufficient conditions for identifiability of the proposed causal estimands, and provide asymptotic statistical guarantees for a proposed inverse probability-weighted estimator, a multiply-robust estimator (for two intervention timepoints), a framework for hypothesis testing, and a computationally scalable implementation. Finally, we apply our framework to data from a recent neuroscience study and show how it provides insight into causal effects of optogenetics on behavior that are obscured by standard analyses.