PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals

• URL: http://arxiv.org/abs/2505.16051v1
• Date: Wed, 21 May 2025 22:02:48 GMT
• Title: PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals
• Authors: Dongze Wu, David I. Inouye, Yao Xie,
• Abstract summary: PO-Flow is a novel continuous normalizing flow (CNF) framework for causal inference.<n>It provides a unified framework for individualized potential outcome prediction, counterfactual predictions, and uncertainty-aware density learning.<n>It consistently outperforms prior methods across a range of causal inference tasks.
• Score: 14.980992014519165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose PO-Flow, a novel continuous normalizing flow (CNF) framework for causal inference that jointly models potential outcomes and counterfactuals. Trained via flow matching, PO-Flow provides a unified framework for individualized potential outcome prediction, counterfactual predictions, and uncertainty-aware density learning. Among generative models, it is the first to enable density learning of potential outcomes without requiring explicit distributional assumptions (e.g., Gaussian mixtures), while also supporting counterfactual prediction conditioned on factual outcomes in general observational datasets. On benchmarks such as ACIC, IHDP, and IBM, it consistently outperforms prior methods across a range of causal inference tasks. Beyond that, PO-Flow succeeds in high-dimensional settings, including counterfactual image generation, demonstrating its broad applicability.

Related papers

• Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching [9.465542901469815]
  Flow matching offers faster generation, higher-quality outputs, and greater flexibility.<n> Conditional Guided Flow Matching (CGFM) extends flow matching by incorporating the outputs of an auxiliary model.<n>For time series forecasting tasks, CGFM integrates historical data as conditions and guidance, constructs two-sided conditional probability paths, and uses a general affine path to expand the space of probability paths.
  arXiv  Detail & Related papers  (2025-07-09T18:03:31Z)
• Probabilistic Forecasting via Autoregressive Flow Matching [1.5467259918426441]
  FlowTime is a generative model for probabilistic forecasting of timeseries data.<n>We decompose the joint distribution of future observations into a sequence of conditional densities, each modeled via a shared flow.<n>We demonstrate the effectiveness of FlowTime on multiple dynamical systems and real-world forecasting tasks.
  arXiv  Detail & Related papers  (2025-03-13T13:54:24Z)
• Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting [43.951394031702016]
  We introduce TSFlow, a conditional flow matching (CFM) model for time series combining Gaussian processes, optimal transport paths, and data-dependent prior distributions.<n>We show that both conditionally and unconditionally trained models achieve competitive results across multiple forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-10-03T22:12:50Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Distributional Gradient Boosting Machines [77.34726150561087]
  Our framework is based on XGBoost and LightGBM. We show that our framework achieves state-of-the-art forecast accuracy.
  arXiv  Detail & Related papers  (2022-04-02T06:32:19Z)
• Flow-based Spatio-Temporal Structured Prediction of Motion Dynamics [21.24885597341643]
  Conditional Flows (CNFs) are flexible generative models capable of representing complicated distributions with high dimensionality and interdimensional correlations. We propose MotionFlow as a novel approach that autoregressively normalizes the output on the temporal input features. We apply our method to different tasks, including prediction, motion prediction time series forecasting, and binary segmentation.
  arXiv  Detail & Related papers  (2021-04-09T14:30:35Z)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)
• RegFlow: Probabilistic Flow-based Regression for Future Prediction [21.56753543722155]
  We introduce a robust and flexible probabilistic framework that allows to model future predictions with virtually no constrains regarding the modality or underlying probability distribution. The resulting method dubbed RegFlow achieves state-of-the-art results on several benchmark datasets, outperforming competing approaches by a significant margin.
  arXiv  Detail & Related papers  (2020-11-30T08:45:37Z)
• Learning Likelihoods with Conditional Normalizing Flows [54.60456010771409]
  Conditional normalizing flows (CNFs) are efficient in sampling and inference. We present a study of CNFs where the base density to output space mapping is conditioned on an input x, to model conditional densities p(y|x)
  arXiv  Detail & Related papers  (2019-11-29T19:17:58Z)

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.