Abstract
This chapter is concerned with methods of causal inference in the presence of unobserved confounders. Three classes of estimators are discussed, namely, local identification using instrumental variables, sensitivity analysis, and estimation of nonparametric bounds. In each case, the response to the core identification problem is to retreat from the standard focus on point identification of the average treatment effect, yet the three approaches characteristically differ in terms of alternative quantities of interest that are considered empirically estimable under more restrictive circumstances. The chapter develops the basic principles underlying the three classes of partial identification estimators and illustrates their empirical application with an analysis of earnings returns to education.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allison, P. D. (1994). Using panel data to estimate the effects of events. Sociological Methods & Research, 23, 174–199.
Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: Evidence from Social Security Administrative records. American Economic Review, 80, 313–335.
Angrist, J. D. (2004). Treatment effect heterogeneity in theory and practice. Economic Journal, 114, C52–C83.
Angrist, J. D., & Evans, W. N. (1998). Children and their parents’ labor supply: Evidence from exogenous variation in family size. American Economic Review, 88, 450–477.
Angrist, J. D., & Imbens, G. W. (1995). Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association, 90, 431–442.
Angrist, J. D., & Krueger, A. B. (2001). Instrumental variables and the search for identification: From supply and demand to natural experiments. Journal of Economic Perspectives, 15, 69–85.
Angrist, J. D., & Lavy, V. (1999). Using Maimonides’ rule to estimate the effect of class size on scholastic achievement. Quarterly Journal of Economics, 114, 533–575.
Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics. An empiricist’s companion. Princeton: Princeton University Press.
Angrist, J. D., & Pischke, J.-S. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24, 3–30.
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444–455.
Ashenfelter, O., & Rouse, C. (1998). Income, schooling, and ability: Evidence from a new sample of identical twins. Quarterly Journal of Economics, 113, 253–284.
Blau, P. M., & Duncan, O. D. (1967). The American occupational structure. New York: Free Press.
Blundell, R., Gosling, A., Ichimura, H., & Meghir, C. (2007). Changes in the distribution of male and female wages accounting for employment composition using bounds. Econometrica, 75, 323–363.
Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with instrumental variables estimation when the correlation between the instruments and the endogeneous explanatory variable is weak. Journal of the American Statistical Association, 90, 443–450.
Card, D. (2001). Estimating the return to schooling: Progress on some persistent econometric problems. Econometrica, 69, 1127–1160.
DiPrete, T. A., & Gangl, M. (2004). Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociological Methodology, 34, 271–310.
Duncan, G. J., Jean Yeung, W., Brooks-Gunn, J., & Smith, J. R. (1998). How much does childhood poverty affect the life chances of children? American Sociological Review, 63, 406–423.
Frank, K. A. (2000). Impact of a confounding variable on a regression coefficient. Sociological Methods & Research, 29, 147–194.
Gangl, M. (2010). Causal inference in sociological research. Annual Review of Sociology, 36, 21–47.
Ganzeboom, H. B. G., & Treiman, D. J. (1996). Internationally comparable measures of occupational status for the 1988 international standard classification of occupations. Social Science Research, 25, 201–239.
Gastwirth, J. L., Krieger, A. M., & Rosenbaum, P. R. (1998). Dual and simultaneous sensitivity analysis for matched pairs. Biometrika, 85, 907–920.
Halaby, C. N. (2004). Panel models in sociological research: Theory into practice. Annual Review of Sociology, 30, 507–544.
Harding, D. J. (2003). Counterfactual models of neighborhood effects: The effect of neighborhood poverty on dropping out and teenage pregnancy. American Journal of Sociology, 109, 676–719.
Heckman, J. J. (1997). Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. Journal of Human Resources, 32, 441–462.
Heckman, J. J., & Urzúa, S. (2010). Comparing IV with structural models: What simple IV can and cannot identify. Journal of Econometrics, 156, 27–37.
Ichino, A., Mealli, F., & Nannicini, T. (2008). From temporary help jobs to permanent employment: What can we learn from matching estimators and their sensitivity? Journal of Applied Econometrics, 23, 305–327.
Imbens, G. W. (2003). Sensitivity to exogeneity assumptions in program evaluation. American Economic Review, 93, 126–132.
Imbens, G. W. (2010). Better LATE than nothing: Some comments on Deaton (2009) and Heckman and Urzua (2009). Journal of Economic Literature, 48, 399–423.
Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–475.
Imbens, G. W., & Rubin, D. B. (1997). Estimating outcome distributions for compliers in instrumental variables models. Review of Economic Studies, 64, 555–574.
Kirk, D. S. (2009). A natural experiment on residential change and recidivism: Lessons from Hurricane Katrina. American Sociological Review, 74, 484–505.
Lash, T. L., Fox, M. P., & Fink, A. K. (2009). Applying quantitative bias analysis to epidemiologic data. New York: Springer.
Lin, D. Y., Psaty, B. M., & Kronmal, R. A. (1998). Assessing the sensitivity of regression results to unmeasured confounders in observational studies. Biometrics, 54, 948–963.
Manski, C. F. (1995). Identification problems in the social sciences. Cambridge: Harvard University Press.
Manski, C. F. (1997). Monotone treatment response. Econometrica, 65, 1311–1334.
Manski, C. F. (2003). Partial identification of probability distributions. New York: Springer.
Manski, C. F. (2007). Identification for prediction and decision. Cambridge: Harvard University Press.
Manski, C. F. (2011). Policy analysis with incredible certitude. Economic Journal, 121, F261–F289.
Manski, C. F., & Nagin, D. S. (1998). Bounding disagreements about treatment effects: A case study of sentencing and recidivism. Sociological Methodology, 28, 99–137.
Manski, C. F., & Pepper, J. V. (2000). Monotone instrumental variables: With an application to the returns to schooling. Econometrica, 68, 997–1010.
Manski, C. F., & Pepper, J. V. (2009). More on monotone instrumental variables. Econometrics Journal, 12, S200–S216.
Manski, C. F., Sandefur, G. D., McLanahan, S., & Powers, D. (1992). Alternative estimates of the effect of family structure during adolescence on high school graduation. Journal of the American Statistical Association, 87, 25–37.
Mauro, R. (1990). Understanding L.O.V.E. (left out variables error): A method for estimating the effects of omitted variables. Psychological Bulletin, 108, 314–329.
Morgan, S. L. (2005). On the edge of commitment: Educational attainment and race in the United States. Stanford: Stanford University Press.
Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference. Methods and principles for social research. Cambridge: Cambridge University Press.
Müller, W., & Karle, W. (1993). Social selection in educational systems in Europe. European Sociological Review, 9, 1–23.
Pearl, J. (2009). Causality. Models, reasoning and inference (2nd ed.). Cambridge: Cambridge University Press.
Robins, J. M. (1999). Association, causation, and marginal structural models. Synthese, 121, 151–179.
Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York: Springer.
Rosenbaum, P. R., & Rubin, D. B. (1983). Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. Journal of the Royal Statistical Society B, 45, 212–218.
Rosenzweig, M. R., & Wolpin, K. I. (2000). Natural ‘natural experiments’ in economics. Journal of Economic Literature, 38, 827–874.
Sharkey, P., & Elwert, F. (2011). The legacy of disadvantage: Multigenerational neighborhood effects on cognitive ability. American Journal of Sociology, 116, 1934–1981.
Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65, 557–586.
Stock, J. H., & Yogo, M. (2005a). Asymptotic distributions of instrumental variables statistics with many weak instruments. In J. H. Stock & D. W. K. Andrews (Eds.), Identification and inference for econometric models: Essays in Honor of Thomas J. Rothenberg (pp. 109–120). Cambridge: Cambridge University Press.
Stock, J., & Yogo, M. (2005b). Testing for weak instruments in linear IV regression. In J. H. Stock & D. W. K. Andrews (Eds.), Identification and inference for econometric models: Essays in Honor of Thomas J. Rothenberg (pp. 80–108). Cambridge: Cambridge University Press.
VanderWeele, T. J. (2011). Sensitivity analysis for contagion effects in social networks. Sociological Methods & Research, 40, 240–255.
VanderWeele, T. J., & Arah, O. A. (2011). Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology, 22, 42–52.
Wagner, G. G., Frick, J. R., & Schupp, J. (2007). The German Socio-Economic Panel Study (SOEP) – Scope, evolution and enhancements. Schmollers Jahrbuch, 127, 139–169.
Acknowledgments
The GSOEP data have kindly been provided by the Deutsche Institut für Wirtschaftsforschung (DIW), Berlin. Of course, the DIW does not bear any responsibility for the uses made of the data, nor the inferences drawn by the author. I thank Stephen Morgan, Jan Brülle, and Fabian Ochsenfeld for helpful comments on an earlier draft of this chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Gangl, M. (2013). Partial Identification and Sensitivity Analysis. In: Morgan, S. (eds) Handbook of Causal Analysis for Social Research. Handbooks of Sociology and Social Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6094-3_18
Download citation
DOI: https://doi.org/10.1007/978-94-007-6094-3_18
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-6093-6
Online ISBN: 978-94-007-6094-3
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)