TY - Journal Article
T1 - Does Regression Produce Representative Estimates of Causal Effects?
JF - American journal of political science
VL - 60
IS - 1
SP - 250
EP - 267
A1 - Peter M. Aronow
A2 - Cyrus Samii
CY - HOBOKEN
PB - Wiley Subscription Services, Inc
PY - 2016
UR - https://bonnus.ulb.uni-bonn.de/SummonRecord/FETCH-LOGICAL-c3185-674f64db4a93f5f66fa37f30f9cb1893df7d7d1018d05dad3f021db710f19a873
N2 - With an unrepresentative sample, the estimate of a causal effect may fail to characterize how effects operate in the population of interest. What is less well understood is that conventional estimation practices for observational studies may produce the same problem even with a representative sample. Causal effects estimated via multiple regression differentially weight each unit's contribution. The "effective sample" that regression uses to generate the estimate may bear little resemblance to the population of interest, and the results may be nonrepresentative in a manner similar to what quasi-experimental methods or experiments with convenience samples produce. There is no general external validity basis for preferring multiple regression on representative samples over quasi-experimental or experimental methods. We show how to estimate the "multiple regression weights" that allow one to study the effective sample. We discuss alternative approaches that, under certain conditions, recover representative average causal effects. The requisite conditions cannot always be met.
KW - AJPS WORKSHOP
KW - Representative sampling
KW - Linear regression
KW - Population estimates
KW - Inference
KW - Regression analysis
KW - Descriptive statistics
KW - Control variables
KW - Multiple regression
KW - Estimators
KW - Term weighting
KW - Government & Law
KW - Social Sciences
KW - Political Science
KW - INFERENCE
KW - REGIMES
KW - RANDOMIZATION
ER -