位置8的问题 Observational approaches to causal inference, like difference-in-differences and regression discontinuity designs, provide estimators that are unbiased for a specific treatment effect when specific assumptions are met because they provide ways to substitute observable quantities for unseeable potential outcomes.TrueFalseDon't Know清除选择

Question

BlackTom AI · Accepted Answer

Restating the question: The prompt asks whether observational approaches to causal inference, such as difference-in-differences and regression discontinuity designs, provide estimators that are unbiased for a specific treatment effect when specific assumptions are met, because they substitute observable quantities for unobservable potential outcomes.
Option 1: True. This statement captures the key idea behind these methods: under certain identifying assumptions, these estimators can consistently identify a well-defined causal effect (often a specific average treatment effect) by using observable data to proxy for unobservable potential outcomes. For difference-in-differences (DiD), the critical assumption is the parallel trends assumption, which allows us to compare changes over time between treated and control groups as if they would have evolved similarly in the absence of treatment. For regression discontinuity (RD), the key assumption is the continuity of potential outcomes at the cutoff, which ensures that units just above and below the threshold are comparable. When these assumptions hold, the estimators target a specific causal parameter and are unbiased for that parameter.
Option 2: False. This would be correct if the statement claimed that these methods are always unbiased or that their unbiasedness holds without any assumptions. In reality, both DiD and RD rely on strong, testable or at least defensible assumptions (parallel trends for DiD, continuity around the cutoff for RD). If these assumptions fail, the estimators can be biased. Therefore, the statement as written is not accurate in general, because unbiasedness is contingent on the assumptions being satisfied.
Option 3: Don't Know. This option would be chosen if one is unsure about the theoretical properties of these methods. However, with a basic understanding that DiD and RD rely on specific assumptions to identify causal effects, one should be able to reason about why unbiasedness is conditional rather than unconditional, making this option inappropriate for a confident answer.
Option 4: (Not applicable in this question as only three options are provided.) This choice cannot be evaluated further since it is not among the listed options. It does not contribute to the reasoning about the true/false nature of the statement.

In summary, the core idea is that these observational designs can yield unbiased estimates for a defined treatment effect provided the necessary identification assumptions hold; without those assumptions, unbiasedness is not guaranteed. The contrasts hinge on whether we require assumptions and whether they hold in a given context, which makes the statement True when the assumptions are satisfied, and False otherwise.

View Explanation

Log in for full answers

Similar Questions

Which of the following is a structural reason why diabetes and heart disease would be associated in the DAG above?

In order to make causal inference in an observational study with confounding by sex, the investigator must adjust for sex in the analysis phase. This is necessary to satisfy the assumption of:

Which of the following assumptions are required for causal inference from observational studies?

It is possible to verify the exchangeability assumption in observational studies.

How do mechanisms help solve the reference class problem, according to Clarke et al.?

位置5的问题 When it is impossible/irresponsible to run a randomized experiment, there is always an approach using observational data that will guarantee a bias-free estimate of the true treatment effect.TrueFalseDon't Know清除选择

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler