Combining observational and experimental data to find heterogeneous treatment effects
This addresses the challenge of detecting heterogeneous effects in high-dimensional settings for applications like online recommendations, though it is incremental as it builds on existing data combination approaches.
The paper tackles the problem of identifying heterogeneous treatment effects when A/B tests are underpowered, especially with high-dimensional covariates, by combining observational and experimental data to reduce power demands. The method is applied to improve Facebook page recommendations, showing practical utility.
Every design choice will have different effects on different units. However traditional A/B tests are often underpowered to identify these heterogeneous effects. This is especially true when the set of unit-level attributes is high-dimensional and our priors are weak about which particular covariates are important. However, there are often observational data sets available that are orders of magnitude larger. We propose a method to combine these two data sources to estimate heterogeneous treatment effects. First, we use observational time series data to estimate a mapping from covariates to unit-level effects. These estimates are likely biased but under some conditions the bias preserves unit-level relative rank orderings. If these conditions hold, we only need sufficient experimental data to identify a monotonic, one-dimensional transformation from observationally predicted treatment effects to real treatment effects. This reduces power demands greatly and makes the detection of heterogeneous effects much easier. As an application, we show how our method can be used to improve Facebook page recommendations.