Program evaluation with remotely sensed outcomes

Ashesh Rambachan, Rahul Singh, Davide Viviano

Abstract:

Economists often estimate treatment effects in experiments using remotely sensed
variables (RSVs), e.g. satellite images or mobile phone activity, in place of directly
measured economic outcomes. A common practice is to use an observational sample to
train a predictor of the economic outcome from the RSV, and then to use its predictions
as the outcomes in the experiment. We show that this method is biased whenever the
RSV is post-outcome, i.e. if variation in the economic outcome causes variation in the
RSV. In program evaluation, changes in poverty or environmental quality cause changes
in satellite images, but not vice versa. As our main result, we nonparametrically identify
the treatment effect by formalizing the intuition that underlies common practice: the
conditional distribution of the RSV given the outcome and treatment is stable across
the samples. Based on our identifying formula, we find that the efficient representation
of RSVs for causal inference requires three predictions rather than one. Valid inference
does not require any rate conditions on RSV predictions, justifying the use of complex
deep learning algorithms with unknown statistical properties. We re-analyze the effect
of an anti-poverty program in India using satellite images.