Estimating long-term treatment effects without long-term outcome data
David Rhys Bernard (Paris School of Economics)
GPI Working Paper No. 11-2020
This paper has been awarded the paper prize of the 2019 Early Career Conference Programme.
Estimating long-term impacts of actions is important in many areas but the key difficulty is that long-term outcomes are only observed with a long delay. One alternative approach is to measure the effect on an intermediate outcome or a statistical surrogate and then use this to estimate the long-term effect. Athey et al. (2019) generalise the surrogacy method to work with multiple surrogates, rather than just one, increasing its credibility in social science contexts. I empirically test the multiple surrogates approach for long-term effect estimation in real-world conditions using long-run RCTs from development economics. In the context of conditional cash transfers for education in Colombia, I find that the method works well for predicting treatment effects over a 5-year time span but poorly over 10 years due to a reduced set of variables available when attempting to predict effects further into the future. The method is sensitive to observing appropriate surrogates.
Other working papers
Time discounting, consistency and special obligations: a defence of Robust Temporalism – Harry R. Lloyd (Yale University)
This paper defends the claim that mere temporal proximity always and without exception strengthens certain moral duties, including the duty to save – call this view Robust Temporalism. Although almost all other moral philosophers dismiss Robust Temporalism out of hand, I argue that it is prima facie intuitively plausible, and that it is analogous to a view about special obligations that many philosophers already accept…
Will AI Avoid Exploitation? – Adam Bales (Global Priorities Institute, University of Oxford)
A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure is instructive…
The scope of longtermism – David Thorstad (Global Priorities Institute, University of Oxford)
Longtermism holds roughly that in many decision situations, the best thing we can do is what is best for the long-term future. The scope question for longtermism asks: how large is the class of decision situations for which longtermism holds? Although longtermism was initially developed to describe the situation of…