Award Abstract # 2149716
Novel Approaches to Estimating the Causal Effect of Policy Interventions in the Presence of Spillovers

NSF Org: SES
Division of Social and Economic Sciences
Recipient: TRUSTEES OF THE UNIVERSITY OF PENNSYLVANIA, THE
Initial Amendment Date: July 18, 2022
Latest Amendment Date: July 18, 2022
Award Number: 2149716
Award Instrument: Standard Grant
Program Manager: Nicholas N Nagle
nnagle@nsf.gov
 (703)292-4490
SES
 Division of Social and Economic Sciences
SBE
 Directorate for Social, Behavioral and Economic Sciences
Start Date: August 1, 2022
End Date: July 31, 2025 (Estimated)
Total Intended Award Amount: $360,000.00
Total Awarded Amount to Date: $360,000.00
Funds Obligated to Date: FY 2022 = $360,000.00
History of Investigator:
  • Nandita Mitra (Principal Investigator)
    nanditam@pennmedicine.upenn.edu
  • Youjin Lee (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Pennsylvania
3451 WALNUT ST STE 440A
PHILADELPHIA
PA  US  19104-6205
(215)898-7293
Sponsor Congressional District: 03
Primary Place of Performance: University of Pennsylvania
423 Guardian Drive/622 Blockley
Philadelphia
PA  US  19104-6021
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): GM1XX56LEP58
Parent UEI: GM1XX56LEP58
NSF Program(s): Methodology, Measuremt & Stats
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9179
Program Element Code(s): 133300
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.075

ABSTRACT

This research project will produce new causal inference methods to estimate spillover effects of public policy interventions. Policy interventions can spill over to portions of the population who are not directly exposed to the policy, but nonetheless live close to regions, such as cities or counties, that are directly affected. Failure to account for spillover effects can have serious implications on the evaluation of public policies, possibly underestimating or overestimating the overall effects of the policy. For instance, a tax on sugar-sweetened beverages in one city may result in beverage drinkers traveling to a nearby city to purchase beverages. This could undermine efforts to assess the effect of the tax on the drinking of sugar-sweetened beverages in the city that implemented the tax. The researchers will investigate the causal effects of policy interventions under varying patterns and degrees of policy exposure in neighboring regions. The methods to be developed will help researchers and policymakers better understand the effect of policy interventions on outcomes of interest in the presence of spillovers. Short courses and workshops will be developed to disseminate the new methods to the broader community. In addition, a graduate student will be mentored, and user-friendly software will be developed and made available.

This research project will develop a novel causal estimator under more relaxed causal assumptions than those commonly used in difference-in-differences approaches. Public policy interventions are commonly evaluated using the difference-in-differences approach. However, this approach does not directly account for spillover effects to neighboring regions, such as nearby cities or states. Using the new identification assumptions, the investigators will develop doubly robust estimators based on flexible modeling and machine learning. The project also will introduce a new causal estimand that can be used to evaluate the effect of a policy intervention under various neighborhood treatment contexts. The researchers will investigate identification conditions that ensure that intervention effects are generalizable and transportable to target populations with different compositions and neighborhood environments. The new methods will be used to assess the impact of the Philadelphia beverage tax on volume sales in Philadelphia and its surrounding counties that did not implement the tax. This research also will provide guidance to other cities considering a similar excise tax. The products of this research, including the statistical software and implementation guidelines, can be used by policy makers to assess any public policy that is implemented in a specific geographic region and has the potential to affect its neighborhoods.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Hettinger, Gary and Lee, Youjin and Mitra, Nandita "Multiply robust difference-in-differences estimation of causal effect curves for continuous exposures" Biometrics , v.81 , 2025 https://doi.org/10.1093/biomtc/ujaf015 Citation Details
Hettinger, Gary and Roberto, Christina and Lee, Youjin and Mitra, Nandita "Doubly robust estimation of policy-relevant causal effects under interference" Journal of the Royal Statistical Society Series C: Applied Statistics , v.74 , 2024 https://doi.org/10.1093/jrsssc/qlae066 Citation Details
Lee, Youjin and Hettinger, Gary and Mitra, Nandita "Policy effect evaluation under counterfactual neighbourhood intervention in the presence of spillover" Journal of the Royal Statistical Society Series A: Statistics in Society , 2025 https://doi.org/10.1093/jrsssa/qnae153 Citation Details

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