Select all of the statements that are true about Difference-In-Differences research designs:

Question

BlackTom AI · Accepted Answer

Restating the prompt: The question asks to select all statements that are true about Difference-In-Differences (DiD) research designs. Notably, the provided data includes only one answer entry and there are no answer options listed to evaluate beyond that single statement.

Option provided for evaluation: "Unlike the \"no uncontrolled confounding\" assumption needed for generic regression methods, the parallel pre-treatment trends assumption for DiD can be empirically examined."

Analysis of this option:
- The core idea of DiD is to compare changes over time between a treatment and a control group, which relies on the parallel trends assumption: in the absence of treatment, the treated and control groups would have followed parallel trajectories over time. This is indeed a central, testable implication of DiD models.
- The statement highlights that this parallel trends assumption can be empirically examined, which is accurate in practice. Researchers often assess pre-treatment trends to gauge whether the assumption is plausibly satisfied, using visual inspections of trends or formal statistical tests.
- The part of the statement that contrasts with a generic regression assumption about no uncontrolled confounding is touching on a methodological distinction. While DiD helps mitigate many confounders that are time-invariant and common shocks, the claim that a generic regression requires a non-existent assumption is overly broad. Standard regression models also rely on assumptions (e.g., exogeneity, no omitted variable bias) that can be violated by uncontrolled confounding. However, this comparison is not central to the DiD literature and could be misleading if interpreted as a universal contrast.
- Overall, this option aligns with established guidance: the parallel trends pre-treatment assumption is a defining feature of DiD models and is something researchers attempt to verify empirically before drawing causal inferences.

Considerations about the absence of other options:
- Since no additional answer options are provided, there is no basis here for evaluating alternative true/false statements about DiD, such as concerns about spillovers, treatment effect heterogeneity, or potential violations like anticipation effects.
- When evaluating DiD statements in general, one would look for mentions of: parallel pre-treatment trends, no spillovers from treated to control units, stable composition over time, no time-varying unobserved confounders that correlate with treatment, and robust checks (placebo tests, varying time windows, multi-period DiD extensions).

In summary, the stated option correctly emphasizes the empirically testable nature of the parallel pre-treatment trends in DiD designs. It also prompts a broader discussion about how DiD relies on assumptions that can and should be examined with data, even though comparing this to a universal \"no uncontrolled confounding\" assumption in all regression contexts needs careful interpretation.

FA25_QTM_110_1 Quiz #8: Difference in Differences

Select all of the statements that are true about Difference-In-Differences research designs:

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类似问题

位置6的问题 Difference-in-differences is only valid when treated and untreated units have identical values for observable pre-treatment features.TrueFalseDon't Know清除选择

The estimate produced by the DiD estimator in this example is:

For these two units, the parallel trends assumption is met.

Simply comparing the treated unit to the untreated unit in the post-treatment period would underestimate the true treatment effect.

Assuming there are no unequal shocks post-treatment, the DiD estimator is unbiased for the true treatment effect for the treated unit.

The estimate produced by the DiD estimator for this example is:

For these two units, the parallel trends assumption is met.

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