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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. Thus the average treatment effect neglects the distribution of the treatment effect. Some parts of the population might be worse off with the treatment ...
When there may exist heterogeneous treatment effects across groups, the LATE is unlikely to be equivalent to the ATE. In one example, Angrist (1989) [16] attempts to estimate the causal effect of serving in the military on earnings, using the draft lottery as an instrument. The compliers are those who were induced by the draft lottery to serve ...
those who only drop-out if assigned to the treatment group; those who only drop-out if assigned to the control group; If the researcher knew the stratum for each subject then the researcher could compare outcomes only within the first stratum and estimate a valid causal effect for that population.
The main problem with estimating the causal effect of such an intervention is the homogeneity of performance to the assignment of treatment (e.g., a scholarship award). Since high-performing students are more likely to be awarded the merit scholarship and continue performing well at the same time, comparing the outcomes of awardees and non ...
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]
3. Check that covariates are balanced across treatment and comparison groups within strata of the propensity score. Use standardized differences or graphs to examine distributions; If covariates are not balanced, return to steps 1 or 2 and modify the procedure; 4. Estimate effects based on new sample
The Rubin Causal Model is a useful a framework for understanding how to estimate the causal effect of the treatment, even when there are confounding variables that may affect the outcome. This model specifies that the causal effect of the treatment is the difference in the outcomes that would have been observed for each individual if they had ...
A quasi-experiment is an empirical study used to estimate the causal impact of an intervention. Quasi-experiments shares similarities with experiments or randomized controlled trials, but specifically lack random assignment to treatment or control. Instead, quasi-experimental designs typically allow assignment to treatment condition to proceed ...