Premium
Applying a propensity score‐based weighting model to interrupted time series data: improving causal inference in programme evaluation
Author(s) -
Linden Ariel,
Adams John L.
Publication year - 2011
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2010.01504.x
Subject(s) - weighting , propensity score matching , covariate , causal inference , estimator , inverse probability weighting , econometrics , flexibility (engineering) , statistics , regression analysis , inference , average treatment effect , outcome (game theory) , computer science , treatment and control groups , regression , interrupted time series , mathematics , medicine , artificial intelligence , mathematical economics , psychiatry , psychological intervention , radiology
Abstract Often, when conducting programme evaluations or studying the effects of policy changes, researchers may only have access to aggregated time series data, presented as observations spanning both the pre‐ and post‐intervention periods. The most basic analytic model using these data requires only a single group and models the intervention effect using repeated measurements of the dependent variable. This model controls for regression to the mean and is likely to detect a treatment effect if it is sufficiently large. However, many potential sources of bias still remain. Adding one or more control groups to this model could strengthen causal inference if the groups are comparable on pre‐intervention covariates and level and trend of the dependent variable. If this condition is not met, the validity of the study findings could be called into question. In this paper we describe a propensity score‐based weighted regression model, which overcomes these limitations by weighting the control groups to represent the average outcome that the treatment group would have exhibited in the absence of the intervention. We illustrate this technique studying cigarette sales in California before and after the passage of Proposition 99 in California in 1989. While our results were similar to those of the Synthetic Control method, the weighting approach has the advantage of being technically less complicated, rooted in regression techniques familiar to most researchers, easy to implement using any basic statistical software, may accommodate any number of treatment units, and allows for greater flexibility in the choice of treatment effect estimators.