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

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In the final pages of On What Matters, Volume II, Derek Parfit comments: ‘We live during the hinge of history… If we act wisely in the next few centuries, humanity will survive its most dangerous and decisive period… What now matters most is that we avoid ending human history.’ This passage echoes Parfit’s comment, in Reasons and Persons, that ‘the next few centuries will be the most important in human history’. …

Desire-Fulfilment and Consciousness – Andreas Mogensen (Global Priorities Institute, University of Oxford)

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Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Paris School of Economics)

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. …