This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed framework consists of six sequential steps: dimensionality reduction, causal identification, positivity assumption violation handling, estimation, refutation and evaluation, and policy optimization. We introduce practical contributions not currently available in existing causal ML toolkits, specifically: (1) a method for detecting and quantifying positivity violations in continuous treatment settings (2) a novel, scalable two-stage dimensionality reduction framework tailored for causal inference with high-dimensional data; (3) the adaptation of sensitivity analysis and estimation methods originally designed for binary treatments to the continuous treatment space and (4) an end-to-end integration of these components into a modular, reproducible workflow. These innovations address real-world challenges in causal inference that are often not covered in theoretical frameworks but frequently encountered in industrial applications. The methodology is validated with a synthetic dataset inspired in a real-world financial debt collection use case, however its design can be applied to analogous problems across different industries. Results demonstrate that the proposed methodology offers a more computationally efficient approach and produces less biased estimates compared to standard methods for problems with continuous treatment and high-dimensional data. A fully functional GitHub repository with documented code and numbered notebooks is made available ensuring reproducibility and practical implementation. The pipeline presented is intended to contribute to closing the gap between academic approaches and practical application in industry contexts where causal ML can be highly beneficial such as the financial sector.
Figures & tables
Method
Precision
Recall
Covariates
Runtime (min)
Baseline
0.05
1
169
>600
Dim. Reduction
0.53
1
15
3
Dim. Reduction & Identification
0.80
1
10
4
Table 1. Comparison of Variable Identification Performance
Methodology
Mean Bias
95% Confidence Interval
Baseline
0.292
[0.279, 0.303]
Adjustment Set Only
0.236
[0.212, 0.257]
Proposed Methodology
0.117
[0.105, 0.131]
Table 2. Comparison of Estimation Bias Across Methodologies
Appendix figures & tables12 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 1. Heterogeneous treatment effects in synthetic generated data - P(Y=1|T=t, X=x)
Figure 2. Heterogeneous treatment effects in synthetic generated data - Observed individual outcomes
Figure 3. Simpson’s paradox in synthetic generated data.
Figure 4. Diagram of the proposed causal pipeline.
Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variety of domains is the continuous treatment setting, where the variable of intervention has a continuous range. This setting is far less explored and represents a substantial shift from the binary treatment setting, with models needing to represent effects across a continuum of treatment values. In this paper, we present the first causal foundation model for the continuous treatment setting. Our model meta-learns the ability to predict causal effects across a wide variety of unseen tasks without additional training or fine-tuning. First, we design a novel prior over data-generating processes with continuous treatment variables in order to generate a rich causal training corpus. We then train a transformer to reconstruct individual treatment-response curves given only observational data, leveraging in-context learning to amortize expensive Bayesian posterior inference. Our model achieves state-of-the-art performance on individual treatment-response curve reconstruction tasks compared to causal models which are trained specifically for those tasks. Inference code (including trained model weights) can be found at https://github.com/layer6ai-labs/CCPFN-inference .
Christopher Stith, Medha Barath, Vahid Balazadeh +2
Layer 6 AI · University of Toronto · Vector Institute
Objective: The growing availability of large-scale observational clinical datasets and challenges in conducting randomized controlled trials have spurred enthusiasm in using causal machine learning (ML) for causal inference in observational data. We present a roadmap for applying causal ML to observational data. Materials and methods: We outline the importance of assessing validity assumptions within available data and applying causal ML responsibly for clinical experts using causal ML and ML practitioners with limited clinical expertise. Observations: Despite advances in causal ML, its limitations remain largely under-appreciated across disciplines. This gap in shared knowledge may impact the validity of findings. Discussion: Causal assumptions must be satisfied and modeling choices justified. Otherwise, these approaches risk producing biased or misleading results, with consequences for clinical research and patient care. Conclusion: Causal ML can be a powerful tool for generating causal hypotheses. We provide a template to strengthen the rigor and interpretability of causal analyses.
Donna Tjandra, Trenton Chang, Sonali Parbhoo +8
Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan, United States · Department of Electrical and Electronic Engineering, Imperial College London, London, UK · Courant Institute of Mathematical Sciences, New York University, New York, New York, United States +8
Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movement in share price, is useful across several domains. However, classical causal estimators tend to assume that all possible interventions are observed, which is infeasible when interventions vary widely, for instance, in the space of all text strings. We adapt a well-known approach of recasting causal inference as a learning problem to address such settings, assuming realizability of the learned function and no unobserved confounding. We show that causal error decomposes into moment-balancing errors of increasing order and exploit structure of the conditional outcome in the confounders to design objectives that directly improve causal estimation. We also show how to project the effect of a high-dimensional treatment onto coarser attributes, which allows a single model to answer several causal questions without additional attribute-specific training. We empirically evaluate our estimators in settings with high-dimensional continuous, discrete, and text treatments. Our experiments demonstrate generalization to held-out treatments, benefits of balancing higher-order moments when required, and projected causal estimates that are competitive with attribute-specific ones.
Nikita Dhawan, Arnav Paruthi, Andrew Kim +3
University of Toronto, Vector Institute · University of Toronto · Vanguard