TY  - JOUR
A1  - Kohler, Ulrich
A1  - Kreuter, Frauke
A1  - Stuart, Elizabeth A.
T1  - Nonprobability Sampling and Causal Analysis
JF  - Annual review of statistics and its application
N2  - The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations.
KW  - causal inference
KW  - generalizability
KW  - self-selection
KW  - nonprobability sampling
KW  - validity
KW  - measurement error
KW  - heterogeneous treatment effects
KW  - big data
Y1  - 2018
U6  - https://doi.org/10.1146/annurev-statistics-030718-104951
SN  - 2326-8298
SN  - 2326-831X
VL  - 6
SP  - 149
EP  - 172
PB  - Annual Reviews
CY  - Palo Alto
ER  -