This paper proposes techniques to enhance the performance of non-causal
models for causal inference using data from randomized experiments. In domains
like advertising, customer retention, and precision medicine, non-causal models
that predict outcomes under no intervention are often used to score individuals
and rank them according to the expected effectiveness of an intervention (e.g,
an ad, a retention incentive, a nudge). However, these scores may not perfectly
correspond to intervention effects due to the inherent non-causal nature of the
models. To address this limitation, we propose causal fine-tuning and effect
calibration, two techniques that leverage experimental data to refine the
output of non-causal models for different causal tasks, including effect
estimation, effect ordering, and effect classification. They are underpinned by
two key advantages. First, they can effectively integrate the predictive
capabilities of general non-causal models with the requirements of a causal
task in a specific context, allowing decision makers to support diverse causal
applications with a "foundational" scoring model. Second, through simulations
and an empirical example, we demonstrate that they can outperform the
alternative of building a causal-effect model from scratch, particularly when
the available experimental data is limited and the non-causal scores already
capture substantial information about the relative sizes of causal effects.
Overall, this research underscores the practical advantages of combining
experimental data with non-causal models to support causal applications.

通过使用实验数据改进非因果推断的非因果模型以提高性能，并展示了其在不同因果任务中的有效性和实际应用优势。