Browse or search publications from faculty affiliated with the lab.
Presidential Address: The Economist as Designer in the Innovation Process for Socially Impactful Digital Products
This paper provides an economic perspective on data-driven innovation in digital products, focusing on the role of complex experiments in measuring and improving social impact. The discussion highlights how tools and insights from economics…
Choosing the “Right” Default Donation Amounts for Each Donor to Balance Multiple Fundraising Objectives
This report describes insights gleaned from the Data Fellows collaboration between PayPal and the Golub Capital Social Impact Lab at Stanford University’s Graduate School of Business. By embedding researchers in PayPal’s charitable giving team,…
The Heterogeneous Impact of Changes in Default Gift Amounts on Fundraising
When choosing whether and how much to donate, potential donors often observe a set of default donation amounts known as an “ask string.” In an experiment with more than 400,000 PayPal users, we replace a relatively unused donation amount ($75) on…
Torch-Choice: A PyTorch Package for Large-Scale Choice Modelling with Python
The torch-choice is an open-source library for flexible, fast choice modeling with Python and PyTorch. torch-choice provides a ChoiceDataset data structure to manage databases flexibly and memory-efficiently. The paper demonstrates constructing a…
Estimating Heterogeneous Treatment Effects with Right-Censored Data via Causal Survival Forests
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in survival…
PayPal Giving Experiments
This report describes insights gleaned from the Data Fellows collaboration among PayPal, Northwestern University’s Kellogg School of Management, the Golub Capital Social Impact Lab at Stanford University’s Graduate School of Business, and…
Synthetic Difference-in-Differences
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference-in-differences and synthetic control methods. Relative to these methods we find, both theoretically and empirically, that this…
Breiman’s Two Cultures: A Perspective from Econometrics
Breiman’s “Two Cultures” paper painted a picture of two disciplines, data modeling, and algorithmic machine learning, both engaged in the analyses of data but talking past each other. Although that may have been true at the time, there is now…
Practitioner’s Guide: Designing Adaptive Experiments
Adaptive experiments present a unique opportunity to more rapidly learn which of many treatments work best, evaluate multiple hypotheses, and optimize for several objectives. For example, they can be used to pilot a large number of potential…
Sampling-based vs. Design-based Uncertainty in Regression Analysis
Consider a researcher estimating the parameters of a regression function based on data for all 50 states in the United States or on data for all visits to a website. What is the interpretation of the estimated parameters and the standard errors?…
Approximate Residual Balancing: Debiased Inference of Average Treatment Effects in High Dimensions
There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on…