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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 ...
Counterfactual thinking is a concept in psychology that involves the human tendency to create possible alternatives to life events that have already occurred; ...
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]
A large part of econometrics is the study of methods for selecting models, estimating them, and carrying out inference on them. The most common econometric models are structural, in that they convey causal and counterfactual information, [2] and are used for policy evaluation.
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)
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]
Determining whether an ATE estimate is distinguishable from zero (either positively or negatively) requires statistical inference. Because the ATE is an estimate of the average effect of the treatment, a positive or negative ATE does not indicate that any particular individual would benefit or be harmed by the treatment.
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.