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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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48 results for path-specific effect

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}-consistency and asymptotic normality for path-specific effect estimation.

The paper develops fair machine learning models using causal path-specific effects.

problem Fairness in machine learning models under causal constraints.
method Lagrange multiplier approach for infinite-dimensional functional estimation, closed-form solutions for constrained optimization.
result Theoretical and flexible semiparametric estimation strategies for fair predictions.

Study finds racial bias in pulse oximeter readings has minimal impact on ICU ventilation rates.

problem Racial disparities in pulse oximeter readings affect clinical decisions in ICU settings.
method Causal inference using path-specific effects and doubly robust estimator.
result Minimal impact of racial discrepancies on invasive ventilation rates, but more pronounced on ventilation duration.

We consider the problem of learning fair decision systems in complex scenarios in which a sensitive attribute might affect the decision along both fair and unfair pathways. We introduce a causal approach to disregard effects along unfair pathways that simplifies and generalizes previous literature. Our method corrects …

2018-02-22abs ↗pdf ↗

Proximal Mediation Analysis with Hidden Recanting Witnesses

problem Identifying path-specific effects in mediation analysis when recanting witnesses are unknown
method Proximal causal inference and semiparametric inference framework
result Developed three novel identification strategies and a semiparametric inference framework

New framework for fairness in continuous protected attributes.

problem Inherited biases in AI predictions with continuous protected attributes.
method Formalizes SP and PP through path-specific partial derivatives, introduces a fair tuning algorithm.
result Existence and construction of fair predictors that satisfy SP along not-allowed paths and PP along allowed paths.

Regulating causal effects through averaged constraints fails to enforce conditional independence.

problem Enforcing conditional independence in regulatory and analytic settings.
method Formulated causal masking as a linear program and analyzed the resulting enforcement problem from both regulator and optimizer perspectives.
result Averaged-constraint optimization often violates stratum-wise requirements while satisfying the averaged one exactly, and detection requires conditional-independence tests.

A new method uses deep learning to evaluate causal theories without strict assumptions.

problem Evaluating causal theories represented as DAGs requires arbitrary assumptions that can bias results.
method Causal-graphical normalizing flows (cGNFs) use deep neural networks to empirically evaluate DAGs without functional form assumptions.
result cGNFs allow flexible, semi-parametric estimation of causal effects from DAGs.

Recently there has been sustained interest in modifying prediction algorithms to satisfy fairness constraints. These constraints are typically complex nonlinear functionals of the observed data distribution. Focusing on the path-specific causal constraints proposed by Nabi and Shpitser (2018), we introduce new theoreti…

2019-10-09abs ↗pdf ↗

The relaxed maximum entropy problem is concerned with finding a probability distribution on a finite set that minimizes the relative entropy to a given prior distribution, while satisfying relaxed max-norm constraints with respect to a third observed multinomial distribution. We study the entire relaxation path for thi…

2013-11-07abs ↗pdf ↗

New method for learning on heterogeneous graphs without meta-paths.

problem Learning on heterogeneous graphs is sensitive to meta-paths choice, leading to poor performance.
method Decompose heterogeneous graph into homogeneous relation-type graphs, combine higher-order representations, use attention mechanisms.
result Our model outperforms state-of-the-art baselines in vertex classification tasks on heterogeneous graph datasets.

The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.

problem Learning cost-optimal sequential decision policies from retrospective data with missing test results.
method Doubly robust Q-learning framework with path-specific inverse probability weights.
result The method reduces testing cost without compromising predictive accuracy.

New method identifies important features and interactions in RF models.

problem Limited theoretical understanding of local feature and interaction importance in RF models.
method Combines global and local analysis to identify frequent feature co-occurrences.
result Proves consistent recovery of true local signal features and interactions.

This work analyzes fairness-accuracy trade-offs using causal methods.

problem Discriminatory behavior in machine learning systems based on sensitive characteristics.
method Introduces path-specific excess loss (PSEL) and causal fairness/utility ratio to quantify trade-offs.
result Shows how enforcing fairness constraints can reduce discrimination while increasing loss.

The leverage effect refers to the generally negative correlation between the return of an asset and the changes in its volatility. There is broad agreement in the literature that the effect should be present for theoretical reasons, and it has been consistently found in empirical work. However, a few papers have pointe…

2019-09-18abs ↗pdf ↗

The Kalinin effectivity is studied and applied to compactifications and Hilbert squares.

problem Understanding Kalinin effectivity in compactifications and its applications.
method Definition, construction methods, and analysis of Kalinin effectivity in various compactifications.
result Wonderful compactifications of hyperplane arrangements and configuration spaces are Kalinin effective.

New method estimates treatment effects in network data, accounting for spillover effects.

problem Treatment effect estimation in networks with spillover effects.
method Augmented inverse probability weighting (AIPW) with cross-fitting and machine learning.
result Semiparametric treatment effect estimator converges at parametric rate and follows Gaussian distribution.

The paper clarifies the distinction between CATE and ITE under ignorability assumptions.

problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.

GADGET framework decomposes global feature effects using recursive partitioning.

problem Misleading global feature effects when feature interactions are present.
method Generalized additive decomposition of global effects (GADGET) based on recursive partitioning.
result Minimizes interaction-related heterogeneity of local feature effects.

A new method estimates treatment effects in mixed groups, improving accuracy.

problem Estimating treatment effects in mixed groups with heterogeneous responses.
method PCM (pre-cluster and merge) approach for nonparametric estimation.
result Asymptotic consistency and significant improvement in accuracy over existing methods.

Study estimates heterogeneous principal causal effects with binary treatments and intermediate variables.

problem Estimating subgroup effects within strata defined by potential values of an intermediate variable.
method Proposes a framework for estimating and forming confidence intervals for heterogeneous principal causal effects under principal ignorability assumption. Develops several estimators with varying robustness properties.
result Established large-sample theory and analyzed bias contributions of each approach.

Study develops method for estimating causal effects in continuous variables.

problem Lack of methods for estimating causal effects in continuous variables.
method Develops a method independent of data generating models for continuous variable interventions.
result Preserves identifiability of data and applies to any generating models.

ICA accurately estimates treatment effects even with confounders.

problem Estimating treatment effects in the presence of confounding variables.
method Uses Independent Component Analysis (ICA) to identify latent sources and estimate mixing coefficients.
result Linear ICA can consistently estimate multiple treatment effects, even with Gaussian confounders, and is more sample-efficient than Orthogonal Machine Learning (OML).

Method improves treatment effect estimation in randomized experiments.

problem Estimating distributional treatment effects in randomized experiments.
method Distributional regression framework with machine learning for variance reduction.
result The proposed method reduces variance of distributional treatment effect estimators.

Dropout introduces both explicit and implicit regularization effects.

problem Understanding the full impact of dropout regularization.
method Disentangled explicit and implicit regularization effects through experiments and analytic simplifications.
result Explicit and implicit regularization effects of dropout are distinct and can be characterized analytically.

New Krylov subspace methods speed up mixed-effects models with crossed random effects.

problem Slow computations for high-dimensional crossed random effects in mixed-effects models.
method Krylov subspace-based methods for generalized mixed-effects models with cross effects.
result Speedups by factors of up to 10,000 in computations for mixed-effects models.

The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases and co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activi…

2018-02-02abs ↗pdf ↗

Previous literature has identified an effect, dubbed the Zumbach effect, that is nonzero empirically but conjectured to be zero in any conventional stochastic volatility model. Essentially this effect corresponds to the property that past squared returns forecast future volatilities better than past volatilities foreca…

2018-09-06abs ↗pdf ↗

Identifies causal effects in LiNGAM models with latent variables.

problem Identifying causal effects in LiNGAM models with latent confounders.
method Complete graphical characterization and efficient algorithms for certification. RICA adaptation for estimation.
result Efficient algorithms and RICA adaptation for estimating causal effects.

Develops HCQRF for estimating heterogeneous treatment effects with censored data.

problem Estimating heterogeneous treatment effects on censored responses with high-dimensional variables.
method Hybrid Censored Quantile Regression Forest (HCQRF) combining random forests and censored quantile regression.
result Demonstrates the effectiveness and stability of HCQRF through simulation studies and real-world application.

Study relaxes identification assumptions for natural direct effects in non-randomized settings.

problem Identifying causal direct effects under unmeasured confounding.
method Developed relaxed conditions for identifying natural direct effects in non-randomized settings.
result Identified natural direct effect under unmeasured confounding conditions.

The Sagnac effect is re-examined using Finslerian geometry.

problem Understanding the Sagnac effect in general relativity.
method Reviewing the geometry of the Sagnac effect with a focus on Finslerian metrics.
result An asymmetry in Finslerian metrics affects the Sagnac effect for both future-pointing null and timelike geodesics.

We study the relationship between national culture and the disposition effect by investigating international differences in the degree of investors' disposition effect. We utilize brokerage data of 387,993 traders from 83 countries and find great variation in the degree of the disposition effect across the world. We fi…

2019-08-30abs ↗pdf ↗

Method estimates treatment effect bounds in sample selection models.

problem Estimating heterogeneous treatment effects in presence of sample selection.
method Debiased/double machine learning approach for non-linear and high-dimensional confounders.
result Substantially tighter effect bounds for younger users.

XTNet estimates complex cross-treatment effects in multi-category, multi-valued settings.

problem Challenges in estimating causal effects for multi-category, multi-valued treatments.
method Dynamic Neural Masking for capturing treatment interactions without restrictive assumptions.
result XTNet consistently outperforms state-of-the-art baselines in multi-category, multi-valued treatment effect estimation.