Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. The synthetic control method (SCM) is a recent addition to the evaluation toolkit but is designed to study a single treated unitand does not easily accommodate staggered adoption . In this paper, we generalize SCM to the staggered adoption setting. Current practice involves fitting SCM separately for each treated unit and then averaging. We show that the average of separate SCM fits does not necessarily achieve good balance for the average of the treated units , leading to possible bias in the estimated effect. We propose "partially pooled" SCM weights that instead minimize both average and state-specific imbalance, and show that the resulting estimator controls biasunder a linear factor model. We also combine our partially pooled SCM weights with traditional fixed effects methods to obtain an augmented estimator that improves over both SCM weighting and fixed effects estimation alone . We assess the performance of the proposed method via extensive simulations and apply our results to the question of whether teacher collective bargaining leads to higher school spending, finding minimal impacts. We implement the proposed method in the augsynth R package.
Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM) , which finds a weighted average of control units that closely balances the treated unit's pre-treatment outcomes . In this paper, we generalize SCM , originally designed to study a single treated unit, to the staggered adoption setting. We first bound the error for the average effect and show that it depends on both the imbalance for each treated unit separately and the imbalance for the average of the treated units . We then propose "partially pooled" SCM weights to minimize a weighted combination of these measures; approaches that focus only on balancing one of the two components can lead to bias. We extend this approach to incorporate unit-level intercept shifts and auxiliary covariates . We assess the performance of the proposed method via extensive simulations and apply our results to the question of whether teacher collective bargaining leads to higher school spending, finding minimal impacts. We implement the proposed method in the augsynth R package.
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Current practice involves fitting SCM separately for
We first bound the error for the average effect and show that it depends on both the imbalance for
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and then averaging. We show that the average of separate SCM fits does not necessarily achieve good balance
separately and the imbalance
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, leading to possible bias in the estimated effect. We
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that instead minimize both average and state-specific imbalance, and show that the resulting estimator controls biasunder a linear factor model. We also combine our partially pooled SCM weights with traditional fixed effects methods to obtain an augmented estimator that improves over both SCM weighting and fixed effects estimation alone
to minimize a weighted combination of these measures; approaches that focus only on balancing one of the two components can lead to bias. We extend this approach to incorporate unit-level intercept shifts and auxiliary covariates