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Using permutation tests to enhance causal inference in interrupted time series analysis.

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Interrupted time series analysis (ITSA) with permutation testing offers a robust method for causal inference. This study confirmed California

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Area of Science:

  • Epidemiology and Public Health
  • Biostatistics
  • Econometrics

Background:

  • Interrupted time series analysis (ITSA) is a quasi-experimental design used to evaluate the impact of interventions on population health outcomes over time.
  • Strengthening internal validity in ITSA often involves comparing the treated unit to a comparable control group.
  • Causal inference in ITSA can be enhanced by employing robust statistical checks.

Purpose of the Study:

  • To introduce and evaluate a permutation testing framework as a novel robustness check for interrupted time series analysis (ITSA).
  • To assess the causal effect of California's Proposition 99 on reducing cigarette sales using ITSA.
  • To improve the reliability of causal claims derived from ITSA by minimizing false positives.

Main Methods:

  • Employed interrupted time series analysis (ITSA) to evaluate the impact of California's Proposition 99 on cigarette sales.
  • Utilized the ITSAMATCH package in Stata to iteratively create comparable control groups by assigning nontreated states to a 'treated' role.
  • Implemented a permutation testing approach by setting a high significance level (P > .40) for identifying balanced matches and using difference-in-differences of trends as the estimator.

Main Results:

  • Only California demonstrated a statistically significant treatment effect, indicating a reduction in cigarette sales.
  • The permutation testing procedure did not yield statistically significant 'treatment effects' for pseudotreated states, reinforcing the validity of California's result.
  • The robustness check supported the conclusion that Proposition 99 was causally linked to decreased cigarette sales in California.

Conclusions:

  • The proposed permutation testing framework serves as a valuable additional robustness check for ITSA, supporting or refuting identified treatment effects.
  • The ease of implementation and demonstrated value suggest this framework should be considered a standard robustness test in multiple group ITSA.
  • The study provides strong evidence for the effectiveness of California's Proposition 99 in reducing cigarette sales, validated by a novel statistical approach.