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A matching framework to improve causal inference in interrupted time-series analysis.

Ariel Linden1

  • 1Linden Consulting Group, LLC, San Francisco, CA, USA.

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Summary

A new method, Interrupted Time-Series Analysis Matching (ITSAMATCH), effectively creates control groups for evaluating interventions. ITSAMATCH offers simpler interpretation and application compared to other methods like synthetic controls.

Keywords:
balancebiascausal inferenceconfoundingcovariatesinterrupted time-series analysismatching

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Interrupted Time-Series Analysis (ITSA) is vital for evaluating interventions on a single unit.
  • Without a control group, ITSA's internal validity is often compromised.
  • A comparable control group is essential for establishing counterfactuals in ITSA.

Purpose of the Study:

  • Introduce ITSAMATCH, a novel framework for creating control groups in ITSA.
  • Enhance the internal validity of ITSA by incorporating a matched control group.
  • Compare ITSAMATCH to existing methods like synthetic controls (SYNTH) and regression adjustment.

Main Methods:

  • ITSAMATCH creates control groups by matching on covariates.
  • The study evaluated California's Proposition 99 using ITSAMATCH.
  • Compared ITSAMATCH with SYNTH and regression adjustment based on covariate balance and treatment effects.

Main Results:

  • ITSAMATCH and SYNTH achieved comparable covariate balance and treatment effect estimates.
  • Regression adjustment failed to detect a treatment effect and showed inconsistent covariate adjustment.
  • ITSAMATCH demonstrated effectiveness in creating a valid control group.

Conclusions:

  • ITSAMATCH provides results comparable to SYNTH but is less complex and easier to interpret.
  • Regression adjustment may obscure true treatment effects.
  • ITSAMATCH is recommended as a primary method for evaluating interventions in multiple-group time-series analysis.