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Composable Causality: A Toolkit for Systematic Time-Series Causal Discovery and Treatment-Effect Benchmarking

NeurIPS 2026 · Probabilistic and scientific modeling › Causal discovery · approach: Causal discovery from time series

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Presents a composable toolkit for generating synthetic multivariate time-series with known causal structure, to support systematic benchmarking of time-series causal discovery and treatment-effect estimation.

Problem

Real-world time series rarely have verified ground-truth causal structure and randomized interventions are usually infeasible, so evaluating time-series causal discovery and treatment-effect estimation relies on synthetic data; existing generators are limited (the abstract is truncated, so specifics

Approach

A composable framework for specifying synthetic time-series data-generating processes with known causal ground truth, enabling systematic benchmarking across causal discovery and treatment-effect tasks.

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