Making treatment effect estimation actionable for personalized decision-making requires overcoming the costs and delays of acquiring necessary features. While many machine learning models estimate Conditional Average Treatment Effects (CATE), they mostly assume that \emph{all} relevant features are readily available at prediction time – a scenario that is rarely realistic. In practice, acquiring features, such as medical tests, can be both expensive and time-consuming, highlighting the need for strategies that select the most informative features for each individual, enhancing decision accuracy while controlling costs. Existing active feature acquisition (AFA) methods, developed for supervised learning, fail to address the unique challenges of CATE, such as confounding, overlap, and the structural similarities of potential outcomes under different treatments. To tackle these challenges, we propose specialised feature acquisition metrics and estimation strategies tailored to the CATE setting. We demonstrate the effectiveness of our methods through experiments on synthetic datasets designed to reflect common biases and data issues. In doing so, this work aims to bridge the gap between cutting-edge CATE estimation techniques and their practical, cost-efficient application in personalised treatment assignment.

While black-box approaches are commonly used for data-driven modeling of dynamical systems, they often obscure a system's underlying behavior and properties, limiting adoption in areas such as medicine and pharmacology. A two-step process of discovering ordinary differential equations (ODEs) and their subsequent mathematical analysis can yield insights into the system's dynamics. However, this analysis may be infeasible for complex equations, and refining the ODE to meet certain behavioral requirements can be challenging. Direct semantic modeling has recently been proposed to address these issues by predicting the system's behavior, such as the trajectory's shape, directly from data, bypassing post-hoc mathematical analysis. In this work, we extend the original instantiation, limited to one-dimensional trajectories and inputs, to accommodate multi-dimensional trajectories with additional personalization, allowing evolution to depend on auxiliary static features (e.g., patient covariates). In a series of experiments, we show how our approach enables practitioners to integrate prior knowledge, understand the dynamics, ensure desired behaviors, and revise the model when necessary.

Causal inference is essential for developing and evaluating medical interventions, yet real-world medical datasets are often difficult to access due to regulatory barriers. This makes synthetic data a potentially valuable asset that enables these medical analyses, along with the development of new inference methods themselves. Generative models can produce synthetic data that closely approximate real data distributions, yet existing methods do not consider the unique challenges that downstream causal inference tasks, and specifically those focused on treatments, pose. We establish a set of desiderata that synthetic data containing treatments should satisfy to maximise downstream utility: preservation of (i) the covariate distribution, (ii) the treatment assignment mechanism, and (iii) the outcome generation mechanism. Based on these desiderata, we propose a set of evaluation metrics to assess such synthetic data. Finally, we present STEAM: a novel method for generating Synthetic data for Treatment Effect Analysis in Medicine that mimics the data-generating process of data containing treatments and optimises for our desiderata. We empirically demonstrate that STEAM achieves state-of-the-art performance across our metrics as compared to existing generative models, particularly as the complexity of the true data-generating process increases.