A scaffolded, human-in-the-loop agentic workflow outperforms less structured LLM baselines and matches hand-tuned methods on several observational causal inference benchmarks.
Estimating Representative Causal Effects with Double Machine Learning
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abstract
Double Machine Learning is widely used to estimate treatment effects from non-experimental data. The "residuals-on-residuals" regression (RORR) is especially popular for its simplicity and computational tractability. However, with heterogeneous treatment effects, the proper interpretation of RORR may not be well understood. We show that, for non-binary treatments with continuous dose-response functions, RORR estimates a conditional variance-weighted average of derivatives evaluated at treatment values not in the observed dataset. This estimand does not equal the Average Causal Derivative (ACD) in general. Hence, even if all units share the same dose-response function, RORR does not estimate an average treatment effect in the population represented by the sample. We propose an alternative estimator for the ACD that is well suited to the large datasets found in applied data science settings. We demonstrate the pitfalls of RORR and the favorable properties of the proposed estimator through an illustrative numerical example and with real-world data from Netflix. Our methodology is used by default in Netflix's observational causal inference platform, where it regularly powers causal research and decision-making at scale.
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A Human-Augmenting Agentic Workflow for Observational Causal Inference
A scaffolded, human-in-the-loop agentic workflow outperforms less structured LLM baselines and matches hand-tuned methods on several observational causal inference benchmarks.