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

2024-05-28

AI Summary

Standard optogenetic analyses discard temporal information, thereby limiting causal inference to coarse-grained effects. To address this, we develop a nonparametric causal inference framework that—novel in neuroscience—adapts the “run-length effect” methodology from mobile health. Our approach introduces history-restricted marginal structural models and a taxonomy of identifiable causal effects, unifying treatment of both open-loop static and closed-loop dynamic intervention designs while robustly handling violations of the positivity assumption. The method integrates inverse-probability weighting, doubly-robust estimation at multiple time points, formal hypothesis testing, and computationally efficient implementation. Applied to real neural data, the framework uncovers fine-grained, temporally resolved causal effects of optogenetic interventions on behavior—effects entirely obscured by conventional analyses. It enjoys statistical consistency and asymptotic theoretical guarantees, substantially expanding the scope of scientifically answerable causal questions in optogenetics and systems neuroscience.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Develops nonparametric causal inference for optogenetics behavioral experiments
• Extends excursion effects to handle dynamic treatment regimes
• Addresses positivity violations in closed-loop optogenetics designs

Innovation

• Non-parametric framework for open-loop optogenetics analysis
• History-restricted marginal structural models for dynamic regimes
• Multiply-robust estimator with asymptotic statistical guarantees