Estimation Station
This estimand represents the average effect of a treatment across the entire population.
What is the Average Treatment Effect (ATE)?
When estimating the causal effect of a policy, we need something we cannot directly observe.
What is a counterfactual?
This assumption ensures that individuals just above and below a threshold differ only in their treatment status, but cannot be tested.
What is continuity of potential outcomes?
This unobserved quantity represents the probability of receiving treatment based on observed pre-treatment covariates.
What is the propensity score?
This technique is considered the gold standard of causal inference, but it is not always feasible.
What is a randomized control trial?
This estimand is typically estimated in regression discontinuity designs and applies only to individuals near a threshold.
What is the Local Average Treatment Effect (LATE)?
When designing a policy to reduce unemployment, this estimand would help you understand the potential benefit of applying the program to all job seekers.
What is the ATE?
This continuous variable, like test scores or income, determines treatment assignment in regression discontinuity designs.
What is running variable?
This method uses propensity scores to adjust for differences between treated and control groups by reweighting observations to mimic randomization.
What is inverse propensity weighting?
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This occurs when a third variable influences both the treatment assignment and the outcome, creating a spurious association that can bias estimates of the causal effect.
What is confounding?
If a scholarship program is offered to students with low SAT scores, you would use this design to measure the program's effectiveness.
What is Regression Discontinuity?
In one type of regression discontinuity design, treatment is perfectly assigned at the threshold, while in the other, some individuals below the threshold receive treatment, and some above do not.
What is sharp versus fuzzy?
This assumption must hold for propensity score methods to work correctly.
What is the assumption of no unobserved confounding?
This type of bias occurs when treatment assignment is correlated with potential outcomes, and randomization helps eliminate it.
What is selection bias?
This estimand measures the effect of a treatment on individuals who are already receiving it and is commonly used to evaluate the effectiveness of current policies or programs.
What is the ATT?
A job training program is voluntary, and participants tend to have higher motivation than non-participants. This method, which uses weights based on the probability of participation, can adjust for differences between the two groups to estimate the program’s causal impact.
What is inverse propensity weighting?
This technique is often used in regression discontinuity designs to estimate the treatment effect when the relationship between the running variable and the outcome is nonlinear near the threshold.
What is local linear regression?
Weighting observations based on their propensity scores selects the most "informative" observations, mimicking the conditions of this method.
What is randomized control trial?
In causal inference, the observed outcome is a weighted combination of these two unobservable quantities, where the weights are determined by the treatment assignment indicator
What are potential outcomes under treatment and control?
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5!
A study evaluates the impact of a tax incentive on small business growth, but the businesses most likely to take advantage of the incentive may also have unmeasured characteristics like entrepreneurial skill or risk tolerance that influence their growth. Estimating the causal effect in this case may be biased due to this problem.
What is selection on unobservables?
This issue arises in regression discontinuity designs when individuals intentionally alter their position on the running variable to fall on the desired side of the threshold, potentially invalidating the results.
What is strategic manipulation of the running variable?
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This decomposition highlights how the treatment effect for the entire population can be expressed as the effect on the treated group, plus a term that reflects differences in untreated outcomes between treated and untreated groups
What is ATE=ATT+(E[Y(0) | D=1]−E[Y(0) | D=0])?