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  1. Difference in differences

    Difference in differences is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. It calculates the effect of a treatment on an outcome by comparing the average change over time in the outcome variable for the treatment group to the average change over time for the control group. Although it is intended to mitigate the effects of extraneous factors and selection bias, depending on how the treatment group is chosen, this method may still be subject to certain biases. Wikipedia

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  2. en.wikipedia.org

    Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3] It calculates the effect of a ...
  3. publichealth.columbia.edu

    A Difference-in-Differences Analysis of Health, Safety, and Greening Vacant Urban Space. American Journal of Epidemiology. 2011. Harman, Jeffrey et al. Changes in per member per month expenditures after implementation of Florida's medicaid reform demonstration. Health Services Research. 2011.
  4. What Is Difference-in-Differences Analysis • Difference-in-Differences (DID) analysis is a statistic technique that analyzes data from a nonequivalence control group design and makes a casual inference about an independent variable (e.g., an event, treatment, or policy) on an outcome variable
  5. timeseriesreasoning.com

    The Difference-in-Differences (DID) regression model can be used to easily and quite elegantly perform all of the above mentioned analysis. The fitted DID model will tell us whether there is evidence of a net-additional effect observed in the treated group that is purely treatment induced, the estimated value of this, whether this estimate is ...
  6. sciencedirect.com

    Observational Studies in Economic Evaluation. D. Polsky, M. Baiocchi, in Encyclopedia of Health Economics, 2014 Before-and-after (difference-in-differences) The before-and-after and the difference-in-differences (DiD) methods are common techniques to address the possibility that there are unobserved covariates which are causing confounding. Both techniques take advantage of multiple ...
  7. diff.healthpolicydatascience.org

    Counterfactual assumption (Parallel Trends) A second key assumption we make is that the change in outcomes from pre- to post-intervention in the control group is a good proxy for the counterfactual change in untreated potential outcomes in the treated group. When we observe the treated and control units only once before treatment \((t=1)\) and once after treatment \((t=2)\), we write this as:
  8. link.springer.com

    Sep 7, 2023Purpose of Review Difference-in-differences analyses are a useful tool for estimating group-level decisions, such as policy changes, training programs, or other non-randomized interventions, on outcomes which occur within the intervention group. However, there is little practical advice on how to apply difference-in-differences to epidemiologic and health data. Here, we provide a tutorial on ...
  9. dimewiki.worldbank.org

    The difference-in-differences method is a quasi-experimental approach that compares the changes in outcomes over time between a population enrolled in a program (the treatment group) and a population that is not (the comparison group). It is a useful tool for data analysis.This page gives an overview of the approach, implementation, and assumptions of differences-in-differences.
  10. tilburgsciencehub.com

    Difference-in-Differences (DiD) is one such quasi-experimental method commonly used to evaluate the causal impact of a treatment or intervention. The DiD framework is used in a wide range of settings, from evaluating the impact of a rise in the minimum wage to assessing how the adoption of online streaming affects music consumption and discovery .
  11. link.springer.com

    One of the pillars of the difference-in-difference is that the policy or change analyzed was an exogenous change, as Lafontaine and Slade point out. It has been called as a "natural experiment," which refers to an analysis that fulfills these three conditions (exogenous change, a group affected by the change, and an unaffected group).

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