Showing Causal inference Show all papers
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026
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Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
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Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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Assuming acyclic, invariant causal relations across environments, two auxiliary environments identify arbitrary nonlinear causal graphs and enable correct counterfactual inference.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Hamiltonian Causal Models separate equations of motion from intervenable mechanisms and define causal effects as interventional path discrepancies, showing entropy production witnesses trajectory-level causal effects invisible to standard average treatment effects.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Linear non-Gaussian cyclic models yield recoverable low-dimensional acyclic summaries representing observational equivalence classes, learnable in cubic time with sample complexity bounds.
Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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DARTS sequentially acquires prognostic covariates within budget constraints to minimize variance while preserving valid causal inference coverage.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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Conditional counterfactual mean embeddings characterize counterfactual outcome distributions via RKHS embeddings, yielding doubly robust estimators with finite-sample convergence rates that recover multimodal structure.
Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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GANICE minimizes averaged Wasserstein risk for conditional interventional distributions using an extended distance and cellwise critic, achieving minimax optimality without density estimation.
Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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A categorical framework defines causal abstractions as natural transformations between Markov functors, unifying prior notions, yielding graphical consistency conditions, and validating high-level do-calculus on low-level graphs with unobserved confounders.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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Prediction-intervention games model leaders choosing predictors against followers intervening on covariates; stable-blanket predictors are provably optimal or near-optimal.
Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026
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Low-Rank Quantile Surfaces model causal direction via monotone transformations yielding low-rank quantile surfaces, proving generic identifiability and outperforming location-scale methods on nonlinear, heteroscedastic data.
Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026
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Calibrated Prediction-Powered Inference post-hoc calibrates black-box predictions on labeled data to improve semisupervised mean estimation efficiency without retraining, with isotonic calibration achieving first-order optimality.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Distribution-free predictive inference for individual treatment effects with continuous covariates requires trivial infinite-length prediction sets.
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
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PAIR-CI is a calibrated nonparametric conditional independence test for incomplete data that uses paired cross-validated imputation to cancel imputation error, controlling false positives near nominal levels and improving causal discovery accuracy over existing methods.
Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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Local data-driven covariate selection via conditional independence remains sound and complete in cyclic causal models, enabling unified cycle-agnostic causal effect estimation.
Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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This paper compares cluster-level and variable-level missingness graphs to derive conditions for recovering joint distributions and macro causal effects from coarse missingness models.
Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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Binary acyclic SCMs with constant observational description length require Θ(n²) additional bits for interventional answers, and interventional oracles leave Θ(n) counterfactual gaps, matching ambiguity bounds.
Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026
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Structural causal bottleneck models assume causal effects depend on low-dimensional cause summaries, enabling flexible dimension reduction via standard algorithms, improved low-sample transfer, and identifiable bottlenecks.
Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026
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