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Estimating the Long-Term Effects of Novel Treatments
Keith Battocchi · Maggie Hei · Greg Lewis · Miruna Oprescu · Vasilis Syrgkanis

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in Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice »

Policy makers often need to estimate the long-term effects of novel treatments, while only having historical data of older treatment options. We propose a surrogate-based approach using a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. Our approach generalizes previous surrogate-style methods, allowing for continuous treatments and serially-correlated treatment policies while maintaining consistency and root-n asymptotically normal estimates under a Markovian assumption on the data and the observational policy. Using a semi-synthetic dataset on customer incentives from a major corporation, we evaluate the performance of our method and discuss solutions to practical challenges when deploying our methodology.

Author Information

Keith Battocchi (Microsoft)
Maggie Hei (Microsoft Research)
Greg Lewis (Microsoft Research)
Miruna Oprescu (Microsoft Research)
Vasilis Syrgkanis (Microsoft Research)

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