Develop causal-graph-based adjustment for causal-forest signals

Develop a causal-graph-based procedure for selecting valid adjustment sets when estimating the causal-forest treatment contrast for each candidate driver, thereby avoiding conditioning on mediators, colliders, or post-treatment variables.

Background

In the MCES implementation, each continuous driver is dichotomized at its median and treated in turn, while the remaining pre-selected drivers are used as controls. Without a stated causal graph, this procedure can condition on variables that bias the estimated treatment contrast.

The paper therefore treats the causal-forest output as one evidence signal rather than an identified average treatment effect. A principled extension would use causal-graph information to determine admissible adjustment sets before estimating and pooling these effects.

References

More importantly, treating each driver in turn as the treatment while using all remaining drivers as controls can, without a stated causal graph, condition on mediators, colliders, or post-treatment variables and thereby bias $\hat{\tau}$; we therefore read $\tilde{e}_{11}$ as one heterogeneity-sensitive evidence signal among many, not as an identified average treatment effect. Supplying a causal graph to choose valid adjustment sets is the principled fix and is left to future work.

— Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational Data  (2608.20187 - Gupta et al., 20 Aug 2026) in Section 3.2.11, “Method 11: Causal Forest”