[Accepted Manuscript] Estimating causal effects: considering three alternatives to difference-in-differences estimation.


O'Neill, S.; Kreif, N.; Grieve, R.; Sutton, M.; Sekhon, J.S.; (2016) [Accepted Manuscript] Estimating causal effects: considering three alternatives to difference-in-differences estimation. Health services & outcomes research methodology. ISSN 1387-3741 DOI: https://doi.org/10.1007/s10742-016-0146-8 (In Press)

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Abstract

Difference-in-differences (DiD) estimators provide unbiased treatment effect estimates when, in the absence of treatment, the average outcomes for the treated and control groups would have followed parallel trends over time. This assumption is implausible in many settings. An alternative assumption is that the potential outcomes are independent of treatment status, conditional on past outcomes. This paper considers three methods that share this assumption: the synthetic control method, a lagged dependent variable (LDV) regression approach, and matching on past outcomes. Our motivating empirical study is an evaluation of a hospital pay-for-performance scheme in England, the best practice tariffs programme. The conclusions of the original DiD analysis are sensitive to the choice of approach. We conduct a Monte Carlo simulation study that investigates these methods' performance. While DiD produces unbiased estimates when the parallel trends assumption holds, the alternative approaches provide less biased estimates of treatment effects when it is violated. In these cases, the LDV approach produces the most efficient and least biased estimates.

Item Type: Article
Faculty and Department: Faculty of Public Health and Policy > Dept of Health Services Research and Policy
URI: http://researchonline.lshtm.ac.uk/id/eprint/2697422

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