The Statistical Software Award

Licheng Liu, Ziyi Liu, Shijian Liu, Tianzhu Qin, Ye Wang, Yiqing Xu

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The Statistical Software Award Committee is pleased to announce fect by Licheng Liu (Stanford), Ziyi Liu (UC Berkeley), Shijian Liu (NYU Stern), Tianzhu Qin (Cambridge University), Ye Wang (UNC-Chapel Hill), and Yiqing Xu (Stanford), as the winner of the 2025 Society for Political Methodology Statistical Software Award. fect is a multi-platform package for estimating causal effects using panel data, available in R, Stata, and Python. It offers a range of counterfactual estimators—including two-way fixed effects, interactive fixed effects, and matrix completion methods—that help researchers address treatment effect heterogeneity and unobserved confounding. Version 2.0.0 also integrates the gsynth package, which is especially useful in settings with a small number of treated units. With diagnostic tools for assessing assumptions and efficient C++-based computation, fect has become a widely used resource for applied work in political science and other social science fields.

Olga Chyzh (chair), Vincent Arel-Bundock, Will Hobbs, Jon Mellon, Sara Dreier

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TBA

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TBA

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The `redist` package by Christopher T Kenny, Cory McCartan, Ben Fifield, and Kosuke Imai of the Algorithm-Assisted Redistricting Methodology (ALARM) Project has rapidly become a key resource for researchers and practitioners seeking to evaluate redistricting plans. `redist` develops statistically grounded and computationally efficient procedures for generating random draws from a distribution of viable redistricting plans, including conditional distributions that satisfy specified requirements for geographic compactness and population parity. The package allows users to test for illegal partisan and racial gerrymandering, a timely and important question in the wake of the 2020 Census and the redistricting cycle that followed. It has had a substantial policy impact, seeing use in legal challenges against and expert analysis of gerrymandering across at least 11 states and, unusually, has also been cited by six Supreme Court justices in oral arguments. In short, it is an ideal recipient of the Society for Political Methodology's 2022 Statistical Software Award.

Olga Chyzh (Toronto), Dean Knox (Penn), Sara Dreier (UNM)

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The Statistical Software Award Committee is pleased to announce fastLink by Ted Enamorado (WUSTL), Kosuke Imai (Harvard), and Ben Fifield (ACLU Legal Analytics Group) as the winner of the 2021 Society for Political Methodology Statistical Software Award. fastLink solves a common problem faced by researchers in political science as well as across the social sciences: merging multiple datasets that do not share unique record identifiers. This problem arises when linking data between surveys and administrative data, among multiple survey rounds, when connecting historical to modern administrative units, and in many other situations. Most applied work relies on manual or ad hoc merging of non-exact matches or deterministic algorithms, none of which can be used to quantify the amount of error in the merge.

Nick Beauchamp (Northeastern), Graeme Blair (UCLA), Clayton Webb (Kansas), and Sarah Bouchat (Northwestern, Chair)