New method improves treatment effect estimation using autoencoders and causal bridge.
problem Inferring causal effects with unobserved confounders.
method Coupling autoencoder with causal bridge to estimate treatment effects.
result Improves accuracy of treatment effect estimates.
New methods identify causal effects without needing complete proxy variables.
problem Identifying causal effects in the presence of unmeasured confounders.
method Partial identification methods that do not require completeness of proxy variables.
result Obtain bounds on causal effects using available proxy variables.
The paper reviews and extends calibration concepts for classification and regression.
problem Formalizing compatibility between probabilistic predictions and outcomes.
method Review and extension of existing calibration concepts, introduction of new concepts.
result Hierarchical relations between calibration concepts for various data types.
Optimal treatment regime uses proxy variables to improve decision-making.
problem Insufficient covariates in observational data lead to confounding issues.
method Proximal causal inference framework and outcome/treatment confounding bridges.
result The proposed optimal treatment regime outperforms existing ones.
Unified framework for DRO using OT with constraints.
problem Handling ambiguity in likelihood ratios and outcomes.
method Unified framework leveraging optimal transport with conditional moment constraints.
result Unified approach enables adversarial perturbation of likelihood ratios and outcomes.
Develops conformalized prediction intervals for bounded continuous outcomes.
problem Predicting continuous outcomes within bounded ranges, especially when models are misspecified.
method Conformal prediction intervals based on transformation regression models, accounting for heteroscedasticity and asymmetry.
result Valid finite-sample coverage confirmed in simulations and real data applications.
Paper resolves the debate on process vs. outcome supervision in reinforcement learning.
problem Distinguishing between process and outcome supervision in reinforcement learning.
method Developed a technical tool (Change of Trajectory Measure Lemma) to show equivalence between outcome and process supervision under standard data coverage assumptions.
result Reinforcement learning through outcome supervision is statistically equivalent to process supervision, up to polynomial factors in horizon.
Study bridges GARCH and NN models for volatility forecasting.
problem Lack of interaction between GARCH and NN approaches for volatility forecasting.
method Established equivalence between GARCH and NN models, introduced GARCH-NN approach.
result GARCH-NN approach enhances volatility forecasting compared to standalone models.
SnapMMD forecasts cell differentiation outcomes from snapshot data.
problem Forecasting cell differentiation outcomes from limited snapshot data.
method SnapMMD learns dynamics by directly fitting the joint distribution of state measurements and observation time with MMD loss, allowing for unknown and state-dependent volatilities.
result SnapMMD delivers accurate forecasts and an R2-style statistic for diagnosing fit.
CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.
problem Challenges in modeling cumulative effects of time-dependent exposures on survival outcomes.
method CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data.
result CENNSurv reveals multi-year lagged and short-term behavioral shifts in survival outcomes.
New method combines strengths of two PCL approaches without density ratio estimation.
problem Estimating causal functions in Proxy Causal Learning with unobserved confounders and proxies.
method Kernel-based doubly robust estimators combining treatment and outcome bridges, density ratio-free.
result Outperforms existing methods on PCL benchmarks, including a prior doubly robust method.
Novel characterization of augmented balancing weights combining outcome and weighting models.
problem Improving estimation accuracy in machine learning models with balancing weights.
method Characterization of augmented balancing weights as linear models, extending to ridge and lasso regression.
result Equivalence and closed-form expressions for specific model choices, providing insights into performance.
Optimization algorithm CoCo improves causal inference from diverse data.
problem Identifying true causal relationships from data with spurious associations.
method CoCo optimizes for causal inference using environments with invariant causal relationships.
result CoCo provides more accurate causal estimates and predictions.
New algorithms learn stable matchings from uncertain user preferences.
problem Learning stable matchings from uncertain user preferences.
method Stochastic multi-armed bandit problem, incentive-aware learning objective, primal-dual formulation.
result Near-optimal regret bounds for learning stable matchings.
New model predicts drug effects across various cell types using causal imputation.
problem Predict drug effects across different cell types given limited data.
method Introduces a novel SCM-based model class with latent factor structure and uses Synthetic Interventions estimator.
result Method outperforms other matrix completion approaches in drug repurposing dataset.
New method modifies diffusions for singular rewards.
problem Handling singular rewards in diffusions.
method Malliavin calculus for non-differentiable rewards.
result Stable and reliable training of diffusions.
New model bridges pricing and reserving for insurance claims.
problem Incomplete claim data due to reporting and settlement delays.
method Develops an occurrence and development model to estimate both claims and premiums.
result Effective resolution of pricing and reserving inconsistencies.
We propose a method to learn causal response representations through direct effect analysis.
problem Uncovering direct causal effects in complex, multivariate settings.
method Our method bridges conditional independence testing with causal representation learning, formulating an optimisation problem to maximise evidence against conditional independence.
result The largest eigenvalue distribution can be bounded by an F F F -distribution, providing testable conditional independence. Study bridges welfare maximization and CATE estimation in policy learning.
problem Tackles the gap between empirical welfare maximization and conditional average treatment effect estimation in policy learning.
method Shows equivalence between EWM and least squares over reparameterized policy class, proposes regularization method.
result Both approaches are interchangeable under common conditions and share theoretical guarantees.
Enhances portfolio optimization under uncertainty using robust multi-objective methods.
problem Uncertainties in real-world portfolio optimization scenarios.
method Robust multi-objective optimization with benchmark comparisons.
result More reliable and adaptable portfolio strategies for market uncertainties.
Study uses SGD to find near-optimal execution cost policies in dynamic markets.
problem Finding optimal execution cost policies in complex markets.
method Stochastic Gradient Descent (SGD) approach to derive near-optimal policies.
result SGD-based policies offer valuable insights and are implementable in volatile markets.
Spatial Deconfounder tackles interference and confounding in spatial data.
problem Interference and unmeasured spatial factors confound causal inference in spatial domains.
method Two-stage method using CVAE with spatial prior to reconstruct confounder, then estimate causal effects.
result Nonparametric identification of direct and spillover effects under weak assumptions.
Study optimizes health incentives to balance efficiency and fairness.
problem Designing health incentives to balance efficiency and fairness.
method Inverse behavioral optimization framework integrating QALY-based incentives and adaptive learning.
result Modern health systems operate near an efficiency-saturated frontier, with small fairness adjustments yielding diminishing returns.
New examples of knots with special bridge positions found.
problem Understanding special types of bridge positions for knots.
method Derived examples of knots with unperturbed weakly reducible non-minimal bridge positions.
result Connected sum of unperturbed bridge positions is unperturbed (a new conjecture).
New methods estimate causal effects through mediators, handling confounding without strict assumptions.
problem Estimating causal effects through mediators while accounting for unmeasured confounding.
method Developed four nonparametric identification strategies using proximal confounding bridge functions, efficient influence function, and quadruply robust estimator. Proposed proximal debiased machine learning approach for high-dimensional nuisance parameters.
result Achieved n \sqrt{n} n -consistency and asymptotic normality for path-specific effect estimation. The paper provides examples of keen weakly reducible bridge spheres for links in b-bridge position.
problem Characterizing and finding examples of keen weakly reducible bridge spheres.
method Analyzing bridge spheres and their properties in terms of compressing disks and width complex.
result Infinitely many examples of keen weakly reducible bridge spheres for links in b-bridge position.
We compute the bridge spectra of cables of 2-bridge knots. We also give some results about bridge spectra and distance of Montesinos knots.
Paper tackles robust batched bandits for heavy-tailed rewards.
problem Clinical trials and other applications with heavy-tailed rewards.
method Proposes robust batched bandit algorithms for heavy-tailed rewards in finite-arm and Lipschitz-continuous settings.
result Heavier-tailed rewards require fewer batches for near-optimal regret in the instance-independent regime and Lipschitz setting.
Any 2-bridge knot in the 3-sphere has a bridge sphere from which any other bridge surface can be obtained by stabilization, meridional stabilization, perturbation and proper isotopy.
Suppose a knot in a 3 3 3 -manifold is in n n n -bridge position. We consider a reduction of the knot along a bridge disk D D D and show that the result is an ( n − 1 ) (n-1) ( n − 1 ) -bridge position if and only if there is a bridge disk E E E such that ( D , E ) (D, E) ( D , E ) is a cancelling pair. We apply this to an unknot K K K , in n n n -bridge position with re…
We define and compare several natural ways to compute the bridge number of a knot diagram. We study bridge numbers of crossing number minimizing diagrams, as well as the behavior of diagrammatic bridge numbers under the connected sum operation. For each notion of diagrammatic bridge number considered, we find crossing …
This work shifts focus from prediction to intervention in social systems.
problem The limitations of focusing solely on prediction in automated decision systems.
method Shift from prediction-focused paradigm to intervention-oriented approach.
result A new perspective unifies statistical frameworks and tools for ADS design, implementation, and evaluation.
New method finds infinitely many surface knots with specific bridge numbers.
problem Finding numerical invariants for surface links.
method Colorings of surface links by keis to prove bridge number existence.
result Existence of infinitely many surface knots with bridge number n for n ≥ 4.
Researchers found infinite links with specific bridge positions.
problem Finding links with minimal bridge positions.
method Applying Takao et al.'s criterion to create links with locally minimal n n n -bridge and globally minimal m m m -bridge positions. result Provided an infinite family of links with specific bridge positions.
End-to-end policy learning method improves CATE estimation.
problem Learning optimal treatment policies from partially observed data.
method Modified causal forest for policy learning.
result Maximizing policy value is equivalent to minimizing CATE.
We show that if K K K is a knot in S 3 S^3 S 3 and Σ Σ Σ is a bridge sphere for K K K with high distance and 2 n 2n 2 n punctures, the number of perturbations of K K K required to interchange the two balls bounded by Σ Σ Σ via an isotopy is n n n . We also construct a knot with two different bridge spheres with 2 n 2n 2 n and 2 n − 1 2n-1 2 n − 1 bridges respecti…
We show that for every integer b ≥ 3 b\geq 3 b ≥ 3 , there exists a link in a b b b -bridge position with respect to a critical bridge sphere. In fact, for each b b b , we construct an infinite family of links which we call square whose bridge spheres are critical.
Two-bridge ribbon knots have symmetric union presentations.
problem Characterizing two-bridge ribbon knots.
method Symmetric union presentations and partial knot analysis.
result Symmetric union presentations for various two-bridge ribbon knots.
We give a locally minimal, but not globally minimal bridge position of a knot, that is, an unstabilized, nonminimal bridge position of a knot. It implies that a bridge position cannot always be simplified so that the bridge number monotonically decreases to the minimal.
Paper explores link and plat presentations, showing equivalence under bridge isotopy.
problem Relationship between links in bridge position and plat presentations.
method Established equivalence between Hilden double coset classes of plat presentations and bridge positions up to isotopy.
result Revealed equivalence of Hilden double coset classes for n-bridge unknot and torus knots in plat position.
We give a complete characterization of those essential simple loops on 2-bridge spheres of 2-bridge links which are null-homotopic in the link complements. By using this result, we describe all upper-meridian-pair-preserving epimorphisms between 2-bridge link groups.
RAGIC predicts stock intervals with risk considerations, improving prediction accuracy and coverage.
problem Limited success in predicting stock market outcomes due to stochastic nature and risk oversight.
method RAGIC uses a GAN with a risk module and temporal module to generate risk-sensitive stock intervals.
result RAGIC achieves a consistent 95% coverage with narrow interval widths, balancing accuracy and risk.
Bridge positions of handlebody-knots are equivalent when stable.
problem Equivalence of bridge positions in handlebody-knots.
method Demonstrated stability of bridge positions.
result Bridge positions of handlebody-knots are stably equivalent.
Unified model bridges mechanistic and non-mechanistic epidemic approaches.
problem Understanding the dynamics of epidemics with flexibility and interpretability.
method A mixture-based model representing time series as Gaussian mixtures, derived from a networked SIR framework.
result The model provides interpretable parameters and low prediction error, validating its use in understanding interventions.
Paper refines generating function for 2-bridge knot groups.
problem Determining the number of epimorphisms between 2-bridge knot groups.
method Refined generating function considering genus and crossing number.
result Improved formula for epimorphisms between 2-bridge knot groups.
The study calculates braid indices for two-bridge knots and proves inequalities.
problem Calculating the braid index of two-bridge knots.
method Proved inequalities and provided average braid index for knots of a given crossing number.
result Average braid index for two-bridge knots with a given crossing number.
We show that there exists an infinite family of knots, each of which has, for each integer k>=0, a destabilized (2k+5)-bridge sphere. We also show that, for each integer n>=4, there exists a knot with a destabilized 3-bridge sphere and a destabilized n-bridge sphere.
FairICP addresses equalized odds fairness for multiple sensitive attributes.
problem Equalized odds fairness for multiple sensitive attributes.
method Adversarial learning with inverse conditional permutation.
result Promotes equalized odds under complex, multi-dimensional sensitive attributes.