CausalRandomForestClassifier?

[LichtLab]

Hi Experts!

On the Relationship Between UpliftRandomForestClassifier and CausalRandomForestClassifier
I'm currently exploring various causal inference models and have a question regarding their nomenclature. While there's a CausalRandomForestRegressor for regression tasks, I haven't directly come across a corresponding CausalRandomForestClassifier for classification. My question is: can UpliftRandomForestClassifier be meaningfully considered the same as what a CausalRandomForestClassifier would be in the context of causal inference? If so, I would be very grateful if you could shed some light on the reasoning behind the differing naming conventions.

Understanding the honesty Setting in UpliftRandomForestClassifier
As I delve into the parameters of UpliftRandomForestClassifier, I've noticed that honesty is set to False by default. This struck me as a bit counter-intuitive, given that the Honest Approach is generally emphasized as a core principle in Causal Forests for robust causal effect estimation. I'm very keen to understand the design philosophy behind this default setting and what considerations led to it.

The Handling of the Honest Approach in CausalRandomForestRegressor
In a related vein, I've observed that CausalRandomForestRegressor doesn't seem to have an explicit parameter for the honest approach. This leads me to wonder: is it correct to assume that the honest approach is inherently built into CausalRandomForestRegressor by default and is not something that can be altered by the user?

Any insights you could offer on these points would be incredibly helpful for my learning. Thank you!

[DanteTrb]

Hi @LichtLab thanks for raising such thoughtful and structured questions. You're touching on nuanced aspects of causal forest design and the philosophical underpinnings of model transparency, which deserve clear treatment. Let me address your three points in order:

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🔍 1. On UpliftRandomForestClassifier vs CausalRandomForestClassifier

You're absolutely right in noticing the naming asymmetry. While both uplift modeling and causal inference share common goals (estimating treatment effects), the UpliftRandomForestClassifier is typically optimized for individualized treatment effect (ITE) estimation under binary classification setups (e.g., treatment vs. control, outcome yes/no).

The absence of a CausalRandomForestClassifier in many libraries reflects the fact that classification tasks are usually not the main objective in formal causal inference — where regression-based estimation of potential outcomes or ITEs is standard. That said, from a modeling perspective:

📌 You can interpret UpliftRandomForestClassifier as a classification-oriented causal forest, assuming you're modeling uplift as a treatment effect classification proxy.

So yes, it is meaningful to think of them in related terms, but the difference lies in what the model optimizes: uplift maximization (classification loss) vs. counterfactual outcome estimation (regression + honesty).

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🔬 2. Why is honest=False by default in UpliftRandomForestClassifier?

Great observation. This may indeed appear counterintuitive since honesty is a core tenet of causal forests as proposed by Athey & Imbens (2016).

The main reasons for honest=False as default in some implementations (like in econml or scikit-uplift) could be:

• Performance trade-off: honesty can increase variance in small data settings, especially in classification tasks where the signal is sparse.
• Historical precedence: many uplift models originated in the marketing world (e.g., targeted advertising), where pure predictive performance (rather than causal interpretability) was prioritized.
• Implementation efficiency: honest splitting doubles data usage during training, which may not be desirable for practitioners with large-scale deployment goals.

That said, from a scientific point of view, setting honest=True is more aligned with causal inference best practices, particularly when interpreting the model beyond black-box predictions.

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⚙️ 3. On CausalRandomForestRegressor and the "missing" honesty parameter

Indeed — the absence of an explicit honest parameter in some libraries can be confusing. However, most well-designed causal forests (e.g., in econml, grf, or causalml) embed the honest splitting strategy implicitly.

If the documentation doesn’t expose this directly, it's often because:

• The honesty procedure is hardcoded as part of the causal estimation guarantee.
• The API assumes honesty is non-negotiable in causal settings (which is defensible).

So yes, your assumption is likely correct: the honest approach is inherently built in and not something that can be turned off (nor should it, in most use cases where interpretability and robustness matter).

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✅ Final Thoughts

Your questions demonstrate a commendable level of design awareness, which is essential when applying causal methods beyond plug-and-play usage. I’d recommend also reviewing:

• Susan Athey’s original work on honest causal trees and forests
• Microsoft’s EconML library documentation
• The grf (Generalized Random Forests) package for R, which exposes honesty very transparently.

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PS: If you found this helpful, feel free to mark it as the accepted answer, always happy to contribute to thoughtful discussions like this. Let's keep raising the bar on rigorous ML practice ✨

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