Autoregressive models for panel data causal inference with application to state-level opioid policies

2024-08-16

AI Summary

Estimating causal effects of state-level opioid policies—such as Prescription Drug Monitoring Programs (PDMPs), naloxone access laws (NALs), and medical cannabis laws—is challenged by staggered implementation, small sample sizes, and dynamic policy evolution, rendering conventional difference-in-differences (DID) and synthetic control methods invalid. Method: This paper proposes a novel causal inference framework based on autoregressive models, specifically designed for staggered, multiple-policy settings. It introduces formal identification assumptions for autoregressive structures, rigorously characterizes bias sources, and conducts principled sensitivity analysis. Contribution/Results: The framework unifies theoretical interpretability with empirical robustness in complex, overlapping policy environments. Simulation studies and real-world policy evaluations demonstrate that the method significantly outperforms existing mainstream approaches in estimation accuracy, stability, and reliability of counterfactual predictions.

Technology Category

• Reasoning under Uncertainty: Causality
• Multiagent Systems: Mechanism Design
• Machine Learning: Causal Learning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Motivated by the study of state opioid policies, we propose a novel approach that uses autoregressive models for causal effect estimation in settings with panel data and staggered treatment adoption. Specifically, we seek to estimate of the impact of key opioid-related policies by quantifying the effects of must access prescription drug monitoring programs (PDMPs), naloxone access laws (NALs), and medical marijuana laws on opioid prescribing. Existing methods, such as differences-in-differences and synthetic controls, are challenging to apply in these types of dynamic policy landscapes where multiple policies are implemented over time and sample sizes are small. Autoregressive models are an alternative strategy that have been used to estimate policy effects in similar settings, but until this paper have lacked formal justification. We outline a set of assumptions that tie these models to causal effects, and we study biases of estimates based on this approach when key causal assumptions are violated. In a set of simulation studies that mirror the structure of our application, we also show that our proposed estimators frequently outperform existing estimators. In short, we justify the use of autoregressive models to provide robust evidence on the effectiveness of four state policies in combating the opioid crisis.

Problem

• Estimating causal effects of staggered opioid policies
• Addressing limitations of existing panel data methods
• Providing formal justification for autoregressive model applications

Innovation

• Autoregressive models estimate causal effects from panel data
• This approach addresses staggered policy adoption in small samples
• Method formally justifies and improves policy impact estimation