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  2. Impact evaluation - Wikipedia

    en.wikipedia.org/wiki/Impact_evaluation

    Different designs require different estimation methods to measure changes in well-being from the counterfactual. In experimental and quasi-experimental evaluation, the estimated impact of the intervention is calculated as the difference in mean outcomes between the treatment group (those receiving the intervention) and the control or comparison ...

  3. Counterfactual thinking - Wikipedia

    en.wikipedia.org/wiki/Counterfactual_thinking

    Counterfactual thinking is a concept in psychology that involves the human tendency to create possible alternatives to life events that have already occurred; ...

  4. Instrumental variables estimation - Wikipedia

    en.wikipedia.org/wiki/Instrumental_variables...

    Informally, in attempting to estimate the causal effect of some variable X ("covariate" or "explanatory variable") on another Y ("dependent variable"), an instrument is a third variable Z which affects Y only through its effect on X. For example, suppose a researcher wishes to estimate the causal effect of smoking (X) on general health (Y). [5]

  5. Difference in differences - Wikipedia

    en.wikipedia.org/wiki/Difference_in_differences

    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]

  6. Synthetic control method - Wikipedia

    en.wikipedia.org/wiki/Synthetic_control_method

    for , the synthetic controls approach suggests using these weights to estimate the counterfactual = = for >. So under some regularity conditions, such weights would provide estimators for the treatment effects of interest.

  7. Propensity score matching - Wikipedia

    en.wikipedia.org/wiki/Propensity_score_matching

    1. Estimate propensity scores, e.g. with logistic regression: Dependent variable: Z = 1, if unit participated (i.e. is member of the treatment group); Z = 0, if unit did not participate (i.e. is member of the control group). Choose appropriate confounders (variables hypothesized to be associated with both treatment and outcome)

  8. Rubin causal model - Wikipedia

    en.wikipedia.org/wiki/Rubin_causal_model

    Rubin defines a causal effect: Intuitively, the causal effect of one treatment, E, over another, C, for a particular unit and an interval of time from to is the difference between what would have happened at time if the unit had been exposed to E initiated at and what would have happened at if the unit had been exposed to C initiated at : 'If an hour ago I had taken two aspirins instead of ...

  9. Simulation heuristic - Wikipedia

    en.wikipedia.org/wiki/Simulation_heuristic

    Significant research on simulation heuristic's application in counterfactual reasoning has been performed by Dale T Miller and Bryan Taylor. For example, they found that if an affectively negative experience, such as a fatal car accident was brought about by an extraordinary event, such as someone who usually goes by train to work but instead ...