Original ArticleIncremental Propensity Score Effects for Time-fixed ExposuresNaimi, Ashley I.a; Rudolph, Jacqueline E.a; Kennedy, Edward H.b; Cartus, Abigailc; Kirkpatrick, Sharon I.d; Haas, David M.e; Simhan, Hyagrivf; Bodnar, Lisa M.c Author Information From the aDepartment of Epidemiology, Emory University, Atlanta, GA bDepartment of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA cDepartment of Epidemiology, University of Pittsburgh, Pittsburgh, PA dSchool of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada eDepartment of Obstetrics & Gynecology, School of Medicine, Indiana University, Indianapolis, IN fMagee Womens Research Institute, University of Pittsburgh, Pittsburgh, PA. Submitted May 18, 2020; accepted November 25, 2020 A.I.N. was supported by NIH grant numbers R01HD093602 and R01HD098130. E.H.K. was supported by NSF DMS grant number 1810979. The study is supported by grant funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): U10 HD063036, RTI International; U10 HD063072, Case Western Reserve University; U10 HD063047, Columbia University; U10 HD063037, Indiana University; U10 HD063041, University of Pittsburgh; U10 HD063020, Northwestern University; U10 HD063046, University of California Irvine; U10 HD063048, University of Pennsylvania; and U10 HD063053, University of Utah. The authors report no conflicts of interest. Correspondence: Ashley I. Naimi, Department of Epidemiology, Emory University, 1518 Clifton Road, Atlanta, GA 30322. E-mail: [email protected]. Epidemiology: March 2021 - Volume 32 - Issue 2 - p 202-208 doi: 10.1097/EDE.0000000000001315 Buy Metrics Abstract When causal inference is of primary interest, a range of target parameters can be chosen to define the causal effect, such as average treatment effects (ATEs). However, ATEs may not always align with the research question at hand. Furthermore, the assumptions needed to interpret estimates as ATEs, such as exchangeability, consistency, and positivity, are often not met. Here, we present the incremental propensity score (PS) approach to quantify the effect of shifting each person’s exposure propensity by some predetermined amount. Compared with the ATE, incremental PS may better reflect the impact of certain policy interventions and do not require that positivity hold. Using the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b), we quantified the relationship between total vegetable intake and the risk of preeclampsia and compared it to average treatment effect estimates. The ATE estimates suggested a reduction of between two and three preeclampsia cases per 100 pregnancies for consuming at least half a cup of vegetables per 1,000 kcal. However, positivity violations obfuscate the interpretation of these results. In contrast, shifting each woman’s exposure propensity by odds ratios ranging from 0.20 to 5.0 yielded no difference in the risk of preeclampsia. Our analyses show the utility of the incremental PS effects in addressing public health questions with fewer assumptions. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.