Counterfactual Forecasting For Panel Data

Music City Center 

We address the challenge of forecasting counterfactual outcomes in panel data characterized by missing observations and latent factor structures with temporal dependencies. Such scenarios are common in causal inference, where estimating unobserved potential outcomes is essential. Our approach extends traditional matrix completion methods by integrating time series dynamics into the latent factors, enhancing the accuracy of counterfactual predictions. Building upon the estimator proposed by Xiong and Pelger [2023], we accommodate both stochastic and deterministic components within the factors, providing a flexible framework for various applications. In the special case of a stationary autoregressive model for the factors, we derive probabilistic error bounds for each unit and forecast horizon, and additionally provide confidence intervals for the forecast values. Empirical evaluations demonstrate that our method outperforms existing techniques, such as multivariate singular spectrum analysis Agarwal et al. [2020], particularly when latent factors exhibit autoregressive behavior. We apply our methodology to the HeartSteps V1 mHealth study, illustrating its effectiveness in forecasting step counts for users receiving activity prompts, thereby leveraging temporal patterns in user behavior.

Counterfactual forecast

Causal forecast

Factor models

Time series forecast

Missing data 

Section on Medical Devices and Diagnostics