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

TL;DR

This paper introduces a new autoregressive modeling approach for estimating causal effects of opioid policies using panel data, addressing challenges of existing methods in dynamic, small-sample policy environments.

Contribution

It provides a formal justification for autoregressive models in causal inference with panel data and demonstrates their advantages over traditional methods through simulations.

Findings

Autoregressive models often outperform existing estimators in simulated settings.

The paper establishes assumptions linking autoregressive models to causal effects.

Simulation results support the effectiveness of the proposed approach.

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 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 show that our proposed estimators frequently outperform existing estimators. In short, we justify the use of autoregressive models to evaluate the effectiveness of four state policies in combating the opioid crisis.