Causal Inference with Unstructured Treatments

• URL: http://arxiv.org/abs/2608.00657v1
• Date: Sat, 01 Aug 2026 13:21:46 GMT
• Title: Causal Inference with Unstructured Treatments
• Abstract summary: Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured.<n>We propose a causal query for unstructured treatments: the maximally influential feature (MIF)<n> Turning the feature on shifts the distribution of treatments toward those that display it, turning it off shifts away, and the MIF effect contrasts the two average potential outcomes.
• Score: 35.842670756205614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions. Consider an instructor writing a course description to attract more students: the treatment is the course description, and the outcome is enrollment. The standard target, the average treatment effect of fixing the treatment to one exact value versus another, runs into two problems. It cannot be estimated, because almost no exact description recurs across courses, leaving no comparable group from which to measure its effect; and it would be of little use even if it could, since no one wants every course to carry the same description. What the instructor actually wants to know is which features of a description raise enrollment, and which of those features can be acted on across many courses. To this end, we propose a causal query for unstructured treatments: the maximally influential feature (MIF), the feature of the treatment that most strongly influences the outcome. We formalize the MIF as a binary feature of the treatment, defined by a feature-scoring function, constrained so that both of its values stay well populated, and chosen to maximize the causal effect it induces. Turning the feature on shifts the distribution of treatments toward those that display it, turning it off shifts away, and the MIF effect contrasts the two average potential outcomes. We study identification conditions for the MIF, develop algorithms to estimate it, and make it actionable through a nudging algorithm that revises a treatment along the MIF into an outcome-improving version. We illustrate the MIF algorithm across applications in text, image, and dynamic treatment sequences.

Related papers

• Causal Inference with Unstructured Outcomes [35.842670756205614]
  We propose a causal query for unstructured outcomes.<n>Key idea is to learn what features of the outcome are most causally affected by the treatment.
  arXiv  Detail & Related papers  (2026-08-04T03:53:04Z)
• An Orthogonal Learner for Individualized Outcomes in Markov Decision Processes [55.93922317950527]
  We develop a novel meta-learner called DRQ-learner.<n>Our DRQ-learner is applicable to settings with both discrete and continuous state spaces.
  arXiv  Detail & Related papers  (2025-09-30T15:49:29Z)
• Uplift modeling with continuous treatments: A predict-then-optimize approach [4.132346971686944]
  The goal of uplift modeling is to recommend actions that optimize specific outcomes by determining which entities should receive treatment.<n>While uplift modeling typically focuses on binary treatments, many real-world applications are characterized by continuousvalued treatments.<n>This paper presents a predictthenoptimize framework to allow for continuous treatments in uplift modeling.
  arXiv  Detail & Related papers  (2024-12-12T12:43:42Z)
• Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner [72.20769640318969]
  Estimating causal quantities from observational data is crucial for understanding the safety and effectiveness of medical treatments.<n>Medical practitioners require not only estimating averaged causal quantities, but also understanding the randomness of the treatment effect as a random variable.<n>This randomness is referred to as aleatoric uncertainty and is necessary for understanding the probability of benefit from treatment or quantiles of the treatment effect.
  arXiv  Detail & Related papers  (2024-11-05T18:14:49Z)
• CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect [22.012322558109574]
  In causal inference, estimating heterogeneous treatment effects (HTE) is critical for identifying how different subgroups respond to interventions. We propose CURLS, a novel rule learning method leveraging HTE, which can effectively describe subgroups with significant treatment effects.
  arXiv  Detail & Related papers  (2024-07-01T06:36:27Z)
• Robust and Agnostic Learning of Conditional Distributional Treatment Effects [44.31792000298105]
  We provide a new robust and model-agnostic methodology for learning the conditional DTE (CDTE) for a class of problems.<n>Our method is model-agnostic in that it can provide the best projection of CDTE onto the regression model class.<n>We investigate the behavior of our proposal in simulations, as well as in a case study of 401(k) eligibility effects on wealth.
  arXiv  Detail & Related papers  (2022-05-23T17:40:31Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• To Impute or not to Impute? -- Missing Data in Treatment Effect Estimation [84.76186111434818]
  We identify a new missingness mechanism, which we term mixed confounded missingness (MCM), where some missingness determines treatment selection and other missingness is determined by treatment selection. We show that naively imputing all data leads to poor performing treatment effects models, as the act of imputation effectively removes information necessary to provide unbiased estimates. Our solution is selective imputation, where we use insights from MCM to inform precisely which variables should be imputed and which should not.
  arXiv  Detail & Related papers  (2022-02-04T12:08:31Z)
• Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time [71.30985926640659]
  We introduce the Disentangled Counterfactual Recurrent Network (DCRN), a sequence-to-sequence architecture that estimates treatment outcomes over time. With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding. We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.
  arXiv  Detail & Related papers  (2021-12-07T16:40:28Z)
• Explaining the Black-box Smoothly- A Counterfactual Approach [12.985758871588454]
  We propose a BlackBox emphCounterfactual Explainer for medical imaging applications. Our framework explains the outcome by gradually emphexaggerating the semantic effect of the given outcome label.
  arXiv  Detail & Related papers  (2021-01-11T23:12:11Z)
• Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects [8.525061716196424]
  Current methods make binary yes-or-no decisions based on the treatment effect of a single outcome dimension. We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities. We utilize deep learning to optimize the desired holistic metric space instead of predicting single-dimensional treatment counterfactual.
  arXiv  Detail & Related papers  (2020-04-21T01:36:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.