University of Chicago
1126 E. 59th Street
Chicago, IL, 60637
Institutional Affiliation: University of Chicago
Information about this author at RePEc
NBER Working Papers and Publications
|January 2020||Combining Matching and Synthetic Controls to Trade off Biases from Extrapolation and Interpolation|
with Maxwell Kellogg, Magne Mogstad, Guillaume Pouliot: w26624
The synthetic control method is widely used in comparative case studies to adjust for differences in pre-treatment characteristics. A major attraction of the method is that it limits extrapolation bias that can occur when untreated units with different pre-treatment characteristics are combined using a traditional adjustment, such as a linear regression. Instead, the SC estimator is susceptible to interpolation bias because it uses a convex weighted average of the untreated units to create a synthetic untreated unit with pre-treatment characteristics similar to those of the treated unit. More traditional matching estimators exhibit the opposite behavior: They limit interpolation bias at the potential expense of extrapolation bias. We propose combining the matching and synthetic control est...
|May 2019||Nonparametric Estimates of Demand in the California Health Insurance Exchange|
with Pietro Tebaldi, Hanbin Yang: w25827
We estimate the demand for health insurance in the California Affordable Care Act marketplace (Covered California) without using parametric assumptions about the unobserved components of utility. To do this, we develop a computational method for constructing sharp identified sets in a nonparametric discrete choice model. The model allows for endogeneity in prices (premiums) and for the use of instrumental variables to address this endogeneity. We use the method to estimate bounds on the effects of changing premium subsidies on coverage choices, consumer surplus, and government spending. We find that a $10 decrease in monthly premium subsidies would cause between a 1.6% and 7.0% decline in the proportion of low-income adults with coverage. The reduction in total annual consumer surplus woul...
|March 2019||Identification of Causal Effects with Multiple Instruments: Problems and Some Solutions|
with Magne Mogstad, Christopher R. Walters: w25691
Empirical researchers often combine multiple instruments for a single treatment using two stage least squares (2SLS). When treatment effects are heterogeneous, a common justification for including multiple instruments is that the 2SLS estimand can still be interpreted as a positively-weighted average of local average treatment effects (LATEs). This justification requires the well-known monotonicity condition. However, we show that with more than one instrument, this condition can only be satisfied if choice behavior is effectively homogenous. Based on this finding, we consider the use of multiple instruments under a weaker, partial monotonicity condition. This condition is implied by standard choice theory and allows for richer heterogeneity. First, we show that the weaker partial monotoni...
|July 2017||Using Instrumental Variables for Inference about Policy Relevant Treatment Effects|
with Magne Mogstad, Andres Santos: w23568
We propose a method for using instrumental variables (IV) to draw inference about causal effects for individuals other than those affected by the instrument at hand. Policy relevance and external validity turns on the ability to do this reliably. Our method exploits the insight that both the IV estimand and many treatment parameters can be expressed as weighted averages of the same underlying marginal treatment effects. Since the weights are known or identified, knowledge of the IV estimand generally places some restrictions on the unknown marginal treatment effects, and hence on the values of the treatment parameters of interest. We show how to extract information about the average effect of interest from the IV estimand, and, more generally, from a class of IV-like estimands that include...