Department of Economics
University of Chicago
1126 E. 59th Street
Chicago IL 60637
Information about this author at RePEc
NBER Working Papers and Publications
|January 2016||Multiple Hypothesis Testing in Experimental Economics|
with John A. List, Yang Xu: w21875
Empiricism in the sciences allows us to test theories, formulate optimal policies, and learn how the world works. In this manner, it is critical that our empirical work provides accurate conclusions about underlying data patterns. False positives represent an especially important problem, as vast public and private resources can be misguided if we base decisions on false discovery. This study explores one especially pernicious influence on false positives—multiple hypothesis testing (MHT). While MHT potentially affects all types of empirical work, we consider three common scenarios where MHT influences inference within experimental economics: jointly identifying treatment effects for a set of outcomes, estimating heterogeneous treatment effects through subgroup analysis, and conducting hyp...
|April 2011||Inference with Imperfect Randomization: The Case of the Perry Preschool Program|
with James J. Heckman, Rodrigo Pinto, Adam Yavitz: w16935
This paper considers the problem of making inferences about the effects of a program on multiple outcomes when the assignment of treatment status is imperfectly randomized. By imperfect randomization we mean that treatment status is reassigned after an initial randomization on the basis of characteristics that may be observed or unobserved by the analyst. We develop a partial identification approach to this problem that makes use of information limiting the extent to which randomization is imperfect to show that it is still possible to make nontrivial inferences about the effects of the program in such settings. We consider a family of null hypotheses in which each null hypothesis specifies that the program has no effect on one of several outcomes of interest. Under weak assumptions, w...
|May 2005||Threshold Crossing Models and Bounds on Treatment Effects: A Nonparametric Analysis|
with Edward Vytlacil: t0307
This paper considers the evaluation of the average treatment effect of a binary endogenous regressor on a binary outcome when one imposes a threshold crossing model on both the endogenous regressor and the outcome variable but without imposing parametric functional form or distributional assumptions. Without parametric restrictions, the average effect of the binary endogenous variable is not generally point identified. This paper constructs sharp bounds on the average effect of the endogenous variable that exploit the structure of the threshold crossing models and any exclusion restrictions. We also develop methods for inference on the resulting bounds.
|April 2005||Treatment Effect Bounds: An Application to Swan-Ganz Catheterization|
with Jay Bhattacharya, Edward Vytlacil: w11263
We reanalyze data from the observational study by Connors et al. (1996) on the impact of Swan-Ganz catheterization on mortality outcomes. The Connors et al. (1996) study assumes that there are no unobserved differences between patients who are catheterized and patients who are not catheterized and finds that catheterization increases patient mortality. We instead allow for such differences between patients by implementing both the bounds of Manski (1990), which only exploits an instrumental variable, and the bounds of Shaikh and Vytlacil (2004), which exploit mild nonparametric, structural assumptions in addition to an instrumental variable. We propose and justify the use of indicators of weekday admission as an instrument for catheterization in this context. We find that in our applicatio...
- Bhattacharya, Jay, Azeem M. Shaikh, and Edward Vytlacil. "Treatment Effect Bounds under Monotonicity Assumptions: An Application to Swan-Ganz Catheterization." American Economic Review 98,2 (2008): 351–56.
- Bhattacharya, Jay & Shaikh, Azeem M. & Vytlacil, Edward, 2012. "Treatment effect bounds: An application to SwanâGanz catheterization," Journal of Econometrics, Elsevier, vol. 168(2), pages 223-243. citation courtesy of