Estimating long-term treatment effects without long-term outcome data
David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 13-2023
The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. Across methods, we find a negative bias in surrogate estimates. For the M-lasso method, in particular, we investigate reasons for this bias and quantify significant precision gains. This provides evidence that the surrogate index method incurs a bias-variance trade-off.
Other working papers
How should risk and ambiguity affect our charitable giving? – Lara Buchak (Princeton University)
Suppose we want to do the most good we can with a particular sum of money, but we cannot be certain of the consequences of different ways of making use of it. This paper explores how our attitudes towards risk and ambiguity bear on what we should do. It shows that risk-avoidance and ambiguity-aversion can each provide good reason to divide our money between various charitable organizations rather than to give it all to the most promising one…
The freedom of future people – Andreas T Schmidt (University of Groningen)
What happens to liberal political philosophy, if we consider not only the freedom of present but also future people? In this article, I explore the case for long-term liberalism: freedom should be a central goal, and we should often be particularly concerned with effects on long-term future distributions of freedom. I provide three arguments. First, liberals should be long-term liberals: liberal arguments to value freedom give us reason to be (particularly) concerned with future freedom…
Towards shutdownable agents via stochastic choice – Elliott Thornley (Global Priorities Institute, University of Oxford), Alexander Roman (New College of Florida), Christos Ziakas (Independent), Leyton Ho (Brown University), and Louis Thomson (University of Oxford)
Some worry that advanced artificial agents may resist being shut down. The Incomplete Preferences Proposal (IPP) is an idea for ensuring that does not happen. A key part of the IPP is using a novel ‘Discounted Reward for Same-Length Trajectories (DReST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectory-lengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose…