Estimating causal effects with matching methods in the presence and absence of bias cancellation
This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the naïve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate the estimation of average causal effects in different ways, depending upon the nature of the missing mechanism. While - contrary to published assertions in the literature - bias cancellation does not generally occur when the multivariate distribution of the errors is symmetric, bias cancellation has been observed to occur for the case where selection into training is the treatment variable, and earnings is the outcome variable. A substantive rationale for bias cancellation is offered, which conceptualizes bias cancellation as the result of a mixture process based on two distinct individual-level decision-making models. While the general properties are unknown, the existence of bias cancellation appears to reduce the average bias in both OLS and matching methods relative to the symmetric distribution case. Analysis of simulated data under a set of difference scenarios suggests that matching methods do better than OLS in reducing that portion of bias that comes purely from the error distribution (i.e., from “selection on unobservables”). This advantage is often found also for the incomplete data case. Matching appears to offer no advantage over OLS in reducing the impact of bias due purely to selection on unobservable variables when the error variables are generated by standard multivariate normal distributions, which lack the bias-cancellation property. (AUTHORS)
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Heckman, James J, 1979.
"Sample Selection Bias as a Specification Error,"
Econometric Society, vol. 47(1), pages 153-61, January.
- J.D. Angrist & Guido W. Imbens & D.B. Rubin, 1993. "Identification of Causal Effects Using Instrumental Variables," NBER Technical Working Papers 0136, National Bureau of Economic Research, Inc.
- James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
When requesting a correction, please mention this item's handle: RePEc:dem:wpaper:wp-2000-013. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Wilhelm)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.