Comprehensive Causal Machine Learning
Type
conference contribution
Date Issued
2024-06-24
Author(s)
Abstract
Uncovering causal effects at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity. Focusing on selection-on-observables, this paper compares three such approaches, the modified causal forest (mcf), the generalized random forest (grf), and double machine learning (dml). It also provides proven theoretical guarantees for the mcf and compares the theoretical properties of the approaches. The findings indicate that dml-based methods excel for average treatment effects at the population level (ATE) and group level (GATE) with few groups, when selection into treatment is not too strong. However, for finer causal heterogeneity, explicitly outcome-centred forest-based approaches are superior. The mcf has three additional benefits: (i) It is the most robust estimator in cases when dml-based approaches underperform because of substantial selectivity; (ii) it is the best estimator for GATEs when the number of groups gets larger; and (iii), it is the only estimator that is internally consistent, in the sense that low-dimensional causal ATEs and GATEs are obtained as aggregates of finer-grained causal parameters.
Language
English
Event Title
COMPIE 2024 - Counterfactual Methods for Policy Impact Evaluation
Event Location
Amsterdam, Netherlands
Event Date
Jun 24 - Jun 26, 2024
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
2024-hutte-ccml-compie.pdf
Size
5.04 MB
Format
Adobe PDF
Checksum (MD5)
ecf54550e48b5a6340d11113d29af9ac