![]() Treatment/intervention and control groups have Parallel Trends in outcome (see below for details)Ĭomposition of intervention and comparison groups is stable for repeated cross-sectional design (part of SUTVA) Intervention unrelated to outcome at baseline (allocation of intervention was not determined by outcome) In order to estimate any causal effect, three assumptions must hold: exchangeability, positivity, and Stable Unit Treatment Value Assumption (SUTVA)1 ![]() ![]() Please refer to Lechner 2011 article for more details. The approach removes biases in post-intervention period comparisons between the treatment and control group that could be the result from permanent differences between those groups, as well as biases from comparisons over time in the treatment group that could be the result of trends due to other causes of the outcome.ĭID usually is used to estimate the treatment effect on the treated (causal effect in the exposed), although with stronger assumptions the technique can be used to estimate the Average Treatment Effect (ATE) or the causal effect in the population. DID requires data from pre-/post-intervention, such as cohort or panel data (individual level data over time) or repeated cross-sectional data (individual or group level). Hence, Difference-in-difference is a useful technique to use when randomization on the individual level is not possible. DID relies on a less strict exchangeability assumption, i.e., in absence of treatment, the unobserved differences between treatment and control groups arethe same overtime. Difference-in-Difference estimation, graphical explanationĭID is used in observational settings where exchangeability cannot be assumed between the treatment and control groups.
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