Papers › Does Residuals-on-Residuals Regression Produce Representative Estimates of Causal Effects?
Does Residuals-on-Residuals Regression Produce Representative Estimates of Causal Effects?
Apoorva Lal, Winston Chou
Double Machine Learning is commonly used to estimate causal effects in large observational datasets. The "residuals-on-residuals" regression estimator (RORR) is especially popular for its simplicity and computational tractability. However, when treatment effects are heterogeneous, the proper interpretation of RORR may not be well understood. We show that, for many-valued treatments with continuous dose-response functions, RORR converges to a conditional variance-weighted average of derivatives evaluated at points not in the observed dataset, which generally differs from the Average Causal Derivative (ACD). Hence, even if all units share the same dose-response function, RORR does not in general converge to an average treatment effect in the population represented by the sample. We propose an alternative estimator suitable for large datasets. We demonstrate the pitfalls of RORR and the favorable properties of the proposed estimator in both an illustrative numerical example and an application to real-world data from Netflix.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections