<p>Yes, but it may need additional work.</p>
<p>If the data come from a <em>randomized controlled trial</em>, then fitting a treatment-response model in every subgroup has the same type of causal interpretation it has in the full sample.</p>
<p>However, in <em>observational data</em>, it is necessary to first estimate the propensity of treatment (i.e., the probability of treatment given the regressors) and use that in the treatment-response model in every subset. This is also known as &quot;local centering&quot; of the treatment indicator. Additionally, local centering of the dependent response variable may improve the performance of the model further.</p>
<p>See Dandl <em>et al.</em> (2022) for more details and comparisons. For the setup in randomized controlled trials, there is also a dedicated interface package <code>model4you</code> that facilitates fitting &quot;personalized&quot; treatment-response models using trees and random forests. See the Seibold <em>et al.</em> publications for details on the software and underlying methods.</p>
<ul>
<li><p>Susanne Dandl, Torsten Hothorn, Heidi Seibold, Erik Sverdrup, Stefan Wager, Achim Zeileis (2022). “What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?.” arXiv:2206.10323, arXiv.org E-Print Archive. <a href="https://doi.org/10.48550/arXiv.2206.10323" rel="nofollow noreferrer">doi:10.48550/arXiv.2206.10323</a></p>
</li>
<li><p>Heidi Seibold, Achim Zeileis, Torsten Hothorn (2019). “model4you: An R Package for Personalised Treatment Effect Estimation.” Journal of Open Research Software, 7(17), 1-6. <a href="https://doi.org/10.5334/jors.219" rel="nofollow noreferrer">doi:10.5334/jors.219</a></p>
</li>
<li><p>Heidi Seibold, Achim Zeileis, Torsten Hothorn (2018). “Individual Treatment Effect Prediction for Amyotrophic Lateral Sclerosis Patients.” Statistical Methods in Medical Research, 27(10), 3104-3125. <a href="https://doi.org/10.1177/0962280217693034" rel="nofollow noreferrer">doi:10.1177/0962280217693034</a></p>
</li>
<li><p>Heidi Seibold, Achim Zeileis, Torsten Hothorn (2016). “Model-Based Recursive Partitioning for Subgroup Analyses.” The International Journal of Biostatistics, 12(1), 45-63. <a href="https://doi.org/10.1515/ijb-2015-0032" rel="nofollow noreferrer">doi:10.1515/ijb-2015-0032</a></p>
</li>
</ul>
Can the algorithm in "partykit" ensure causal explanation?
<p>Can the exposure-response relationship estimated within each subgroup, generated by using &quot;partykit&quot;, have causal interpretation?</p>