Causal Inference in Sociological Research

被引:230
作者
Gangl, Markus [1 ]
机构
[1] Univ Wisconsin, Dept Sociol, Madison, WI 53706 USA
来源
ANNUAL REVIEW OF SOCIOLOGY, VOL 36 | 2010年 / 36卷
关键词
counterfactual model; treatment effects; identification; endogeneity; unobserved heterogeneity; nonparametric estimation; INSTRUMENTAL VARIABLES ESTIMATION; MARGINAL STRUCTURAL MODELS; PROPENSITY SCORE; MATCHING ESTIMATORS; SCIENTIFIC MODEL; UNITED-STATES; IDENTIFICATION; REGRESSION; HETEROGENEITY; POLICY;
D O I
10.1146/annurev.soc.012809.102702
中图分类号
C91 [社会学];
学科分类号
030301 ; 1204 ;
摘要
Originating in econometrics and statistics, the counterfactual model provides a natural framework for clarifying the requirements for valid causal inference in the social sciences. This article presents the basic potential outcomes model and discusses the main approaches to identification in social science research. It then addresses approaches to the statistical estimation of treatment effects either under unconfoundedness or in the presence of unmeasured heterogeneity. As an update to Winship & Morgan's (1999) earlier review, the article summarizes the more recent literature that is characterized by a broader range of estimands of interest, a renewed interest in exploiting experimental and quasi-experimental designs, and important progress in the areas of semi- and nonparametric estimation of treatment effects, difference-in-differences estimation, and instrumental variable estimation. The review concludes by highlighting implications of the recent econometric and statistical literature for sociological research practice. Avoiding causal language when causality is the real subject of our investigation either renders the research irrelevant or permits it to be undisciplined by the rules of scientific inference .... Rather we should draw causal inferences where they seem appropriate but also provide the reader with the best and most honest estimate of the uncertainty of that inference. (King et al. 1994, p. 76)
引用
收藏
页码:21 / 47
页数:27
相关论文
共 155 条
[1]  
Abbring JH, 2007, HBK ECON, V2, P5145, DOI 10.1016/S1573-4412(07)06072-2
[2]   USING PANEL-DATA TO ESTIMATE THE EFFECTS OF EVENTS [J].
ALLISON, PD .
SOCIOLOGICAL METHODS & RESEARCH, 1994, 23 (02) :174-199
[3]  
Angrist J., 1999, Handb. Labor Econ., V3, P1277, DOI [10.1016/S1573-4463(99)03004-7, DOI 10.1016/S1573-4463(99)03004-7]
[4]   Using Maimonides' rule to estimate the effect of class size on scholastic achievement [J].
Angrist, JD ;
Lavy, V .
QUARTERLY JOURNAL OF ECONOMICS, 1999, 114 (02) :533-575
[5]  
ANGRIST JD, 1990, AM ECON REV, V80, P313
[6]  
Angrist JD, 1996, J AM STAT ASSOC, V91, P444, DOI 10.2307/2291629
[7]  
Angrist JD, 1998, AM ECON REV, V88, P450
[8]   2-STAGE LEAST-SQUARES ESTIMATION OF AVERAGE CAUSAL EFFECTS IN MODELS WITH VARIABLE TREATMENT INTENSITY [J].
ANGRIST, JD ;
IMBENS, GW .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1995, 90 (430) :431-442
[9]  
Angrist JD, 2009, MOSTLY HARMLESS ECONOMETRICS: AN EMPIRICISTS COMPANION, P1
[10]   Instrumental variables and the search for identification: From supply and demand to natural experiments [J].
Angrist, JD ;
Krueger, AB .
JOURNAL OF ECONOMIC PERSPECTIVES, 2001, 15 (04) :69-85