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 effective is (more) money? Randomizing unconditional cash transfer amounts in the US – Ania Jaroszewicz (University of California San Diego), Oliver P. Hauser (University of Exeter), Jon M. Jachimowicz (Harvard Business School) and Julian Jamison (University of Oxford and University of Exeter)
We randomized 5,243 Americans in poverty to receive a one-time unconditional cash transfer (UCT) of $2,000 (two months’ worth of total household income for the median participant), $500 (half a month’s income), or nothing. We measured the effects of the UCTs on participants’ financial well-being, psychological well-being, cognitive capacity, and physical health through surveys administered one week, six weeks, and 15 weeks later. While bank data show that both UCTs increased expenditures, we find no evidence that…
Dispelling the Anthropic Shadow – Teruji Thomas (Global Priorities Institute, University of Oxford)
There are some possible events that we could not possibly discover in our past. We could not discover an omnicidal catastrophe, an event so destructive that it permanently wiped out life on Earth. Had such a catastrophe occurred, we wouldn’t be here to find out. This space of unobservable histories has been called the anthropic shadow. Several authors claim that the anthropic shadow leads to an ‘observation selection bias’, analogous to survivorship bias, when we use the historical record to estimate catastrophic risks. …
Three mistakes in the moral mathematics of existential risk – David Thorstad (Global Priorities Institute, University of Oxford)
Longtermists have recently argued that it is overwhelmingly important to do what we can to mitigate existential risks to humanity. I consider three mistakes that are often made in calculating the value of existential risk mitigation: focusing on cumulative risk rather than period risk; ignoring background risk; and neglecting population dynamics. I show how correcting these mistakes pushes the value of existential risk mitigation substantially below leading estimates, potentially low enough to…