Using LLMs to Directly Guess Conditional Expectations Can Improve Efficiency in Causal Estimation
利用大语言模型直接猜测条件期望以改善因果估计效率
We propose a simple yet effective use of LLM-powered AI tools to improve causal estimation. In double machine learning, the accuracy of causal estimates of the effect of a treatment on an outcome in the presence of a high-dimensional confounder depends on the performance of estimators of conditional expectation functions. We show that predictions made by generative models trained on historical data can be used to improve the performance of these estimators relative to…