Autoregressive models for panel data causal inference with application to state-level opioid policies
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
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| _version_ | 1866917014018195456 |
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| author | Antonelli, Joseph Rubinstein, Max Agniel, Denis Smart, Rosanna Stuart, Elizabeth Cefalu, Matthew Schell, Terry Eagan, Joshua Stone, Elizabeth Griswold, Max Griffin, Beth Ann |
| author_facet | Antonelli, Joseph Rubinstein, Max Agniel, Denis Smart, Rosanna Stuart, Elizabeth Cefalu, Matthew Schell, Terry Eagan, Joshua Stone, Elizabeth Griswold, Max Griffin, Beth Ann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09012 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Autoregressive models for panel data causal inference with application to state-level opioid policies Antonelli, Joseph Rubinstein, Max Agniel, Denis Smart, Rosanna Stuart, Elizabeth Cefalu, Matthew Schell, Terry Eagan, Joshua Stone, Elizabeth Griswold, Max Griffin, Beth Ann Methodology Applications 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. |
| title | Autoregressive models for panel data causal inference with application to state-level opioid policies |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2408.09012 |