New method improves IV estimation with many weak and invalid instruments.
problem Identification in linear IV models with unknown validity.
method Non-convex penalized approaches, surrogate sparsest penalty.
result Advantages over other IV estimators in selection consistency and weak IV strength conditions.
Ivy combines weak IV candidates to estimate causal effects robustly.
problem Estimating causal effects from observational data using weak or invalid IV candidates.
method Ivy synthesizes multiple weak IV candidates into a robust summary.
result Ivy produces more reliable causal effect estimates compared to allele scores.
Paper develops a new estimator for panel data with endogenous treatments, improving causal inference.
problem Challenges in causal inference for static panel data with endogenous treatments and confounding variables.
method Develops Double Machine Learning (DML) estimator for static panel models with endogenous treatments (panel IV DML). Introduces weak-identification diagnostics.
result Panel IV DML estimator improves estimation accuracy and delivers more reliable inference under weak identification.
Combines IV and observational data to estimate CATEs with low compliance and unobserved confounding.
problem Estimating CATEs in personalized medicine and analytics with observational data and weak IVs.
method Two-stage framework: first learns biased CATEs from observational data, then corrects using IV data.
result Effective in estimating CATEs with low compliance and unobserved confounding.
BRACE addresses noncompliance in bandits, offering methods for recommendation and treatment policies.
problem Noncompliance in bandit problems complicates learning objectives and treatment effects.
method BRACE formalizes objective-choice, identifies direct-control regimes, and proposes a phase-doubling algorithm for IV inversion.
result BRACE delivers valid policy values and structural uncertainty, even under weak identification and homogeneity failure.
We present a second iteration of a machine learning approach to static code analysis and fingerprinting for weaknesses related to security, software engineering, and others using the open-source MARF framework and the MARFCAT application based on it for the NIST's SATE IV static analysis tool exposition workshop's data…
Paper presents a new policy gradient theorem using weak derivatives for reinforcement learning.
problem Continuous state-action reinforcement learning problems.
method Introduced an alternative policy gradient theorem using weak derivatives.
result The new approach yields algorithms that converge almost surely to stationary points of the value function.
A new QHR model extends HR model with a quadratic variance function.
problem Modeling volatility with greater flexibility and stationarity.
method Introducing a quadratic variance function to the HR model, maintaining Markovian property.
result Stationary distribution of the QHR model is Pearson type IV.
BGM-IV uses AI to estimate causal effects in complex data.
problem Estimating causal effects in high-dimensional, nonlinear settings with endogeneity.
method Structured latent generative modeling for posterior inference in a causally structured latent space.
result BGM-IV outperforms existing methods in high-dimensional covariate regimes.
Paper discovers valid IVs from data without domain knowledge.
problem Inferring causal effects from observational data with latent confounders.
method Data-driven algorithm based on partial ancestral graphs (PAGs).
result Discovering valid IVs leads to accurate causal effect estimation.
A new method learns IV representation from data to estimate causal effects.
problem Inferring causal effects from observational data with latent confounders.
method Disentangled representation learning using Variational AutoEncoder (VAE).
result The proposed method outperforms existing IV-based estimators and VAE-based estimators.
DML-IV improves IV regression for learning decision policies by reducing bias.
problem Spurious correlations in offline datasets caused by hidden confounders.
method Double/debiased machine learning (DML) framework to reduce bias in two-stage IV regression.
result DML-IV outperforms state-of-the-art methods and learns high-performing policies.
Flow IV uses IVs to infer counterfactuals in complex models.
problem Identifying causal effects and counterfactual reasoning in nonseparable outcome models.
method Utilizes instrumental variables and normalizing flows to estimate and infer counterfactual outcomes.
result Identifies a method to make causal inferences from observed data in nonseparable models.
Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canonical examples of settings where a closed-form expression exists for the causal effect of one variable on another, given the presence of a th…
Method selects valid IVs from a large set using clustering and test of overidentifying restrictions.
problem Selecting valid instrumental variables from a large set of candidates.
method Agglomerative hierarchical clustering combined with a test of overidentifying restrictions.
result Achieves oracle properties when the largest group of IVs is valid.
Develops framework for estimating and improving DTRs with time-varying IV in the presence of unmeasured confounding.
problem Estimating DTRs from observational data with unmeasured confounding.
method Time-varying instrumental variable (IV) framework for estimating and improving DTRs.
result IV-optimal and IV-improved DTRs perform better than DTRs assuming no unmeasured confounding.
We propose a direction of arrival (DOA) estimation method that combines sound-intensity vector (IV)-based DOA estimation and DNN-based denoising and dereverberation. Since the accuracy of IV-based DOA estimation degrades due to environmental noise and reverberation, two DNNs are used to remove such effects from the obs…
Novel quasi-Bayesian method for IV regression using machine learning models.
problem Uncertainty quantification in IV regression with machine learning models.
method Quasi-Bayesian procedure based on kernelized IV models and dual formulation.
result Established minimax optimal contraction rates and scalable inference algorithm.
Improved IV estimates by weighting on compliance reduces noise in treatment effect estimation.
problem Noisy IV estimates in settings with non-random treatment receipt.
method Weighting observations by estimated compliance, leveraging machine learning for compliance estimation.
result Compliance weighting reduces IV variance, improving precision of treatment effect estimates.
Proposes MRIV framework for unbiased CATE estimation using binary IVs.
problem Bias in estimating CATEs due to unobserved confounders.
method Multiply robust machine learning framework (MRIV) for binary IVs.
result MRIV yields multiple robust convergence rates and outperforms existing methods.
Study integrates implied Hurst exponent into IV models for better market efficiency.
problem Capturing market efficiency in IV models based on moneyness.
method Developed an IV model integrating implied Hurst exponent H, optimizing across multiple indexes.
result Model outperforms SABR and fSABR in accuracy, capturing IV-H dynamics.
Develops a statistical test for IV, improving feature selection reliability.
problem Lack of statistical justification in conventional IV-based feature selection.
method Establishes connection with Jeffreys divergence and proposes a nonparametric hypothesis test.
result The J-Divergence test provides rigorous guarantees and is more reliable than traditional IV thresholds.
The study finds a liquidity premium in stock returns, but only after correcting for microstructure noise.
problem The positive association between expected idiosyncratic volatility and expected stock returns.
method Developed a novel method to eliminate microstructure influences from stock returns and estimate idiosyncratic volatility.
result The liquidity premium in value-weighted portfolios is driven by liquidity in the prior month after correcting for microstructure noise.
For later use in subsequent upcoming arxiv.org prepublications, basic foundational material on local, smooth or real analytic, CR-generic submanifolds of complex Euclidean spaces is developed from scratch, with strong emphasis on the interplay between extrinsic and intrinsic aspects, a constructive option that commands…
This study examines how earnings announcements affect option volatility and pricing.
problem The impact of earnings announcements on option volatility and pricing.
method Analysis of extremely short-term options data to study bimodality and concavity in IV curves.
result Investors pay a premium to hedge against extreme volatility during earnings announcements in the presence of concave IV smiles.
Spectral feature learning improves IV regression for causal effect estimation.
problem Estimating causal effects in the presence of hidden confounders.
method Two-stage least squares estimator based on spectral features.
result Performance of the method depends on strong spectral alignment and slow eigenvalue decay.
Symmetry reduction of Painlevé IV to Flaschka-Newell Painlevé II
problem Isomonodromic deformation problem associated with rank-two meromorphic connections
method Symmetry Ψ(−λ)=σ1Ψ(λ)σ1 result Induced isomonodromic dynamics coincides with Flaschka-Newell Painlevé II hierarchy
The purpose of this erratum is to fill a gap in the proof of the `Composite Braid Theorem' in the manuscript "Studying Links Via Closed Braids IV: Composite Links and Split Links (SLVCB-IV)", Inventiones Math, \{bf 102\} Fasc. 1 (1990), 115-139. The statement of the theorem is unchanged. The gap occurs on page 135, lin…
This paper explores how IV methods can improve Q-function estimates in offline policy evaluation.
problem Confounding in estimating Q-function using reinforcement learning.
method Integrates IV techniques into offline policy evaluation (OPE) to improve Q-function estimates.
result State-of-the-art OPE methods are closely matched in performance by some IV methods.
Estimates price elasticity from autocorrelated time series using causal graphs.
problem Inconsistent IV estimators in autocorrelated time series data.
method Model equilibrium with unobserved confounders, derive DAG, and use graphical inference for valid IV estimators.
result Valid IV estimators improve understanding of economic dynamics.
WamOL uses PINNs to efficiently calibrate IVS from sparse data.
problem Calibrating time-dependent IVS from sparse market data.
method Physics-Informed Neural Networks (PINNs) with adaptive reweighting.
result WamOL outperforms in calibrating intraday IVS from uneven data.
This note corrects an erroneous statement in Lemma 3.8 of the author's paper Embedded Contact Homology and Seiberg-Witten Floer Homology IV which was published in Volume 14 of Geometry and Topology in 2009.
We examine the efficiency of the Asymmetric Power ARCH (APARCH) model in the case where the residuals follow the standardized Pearson type IV distribution. The model is tested with a variety of loss functions and the efficiency is examined via application of several statistical tests and risk measures. The results indi…
AI uses language models to find instrumental variables quickly.
problem Finding valid instrumental variables is a challenging and heuristic process.
method Uses large language models to search for new instrumental variables through narratives and counterfactual reasoning.
result Demonstrates the effectiveness of multi-step and role-playing prompting strategies for LLMs.
Generates consistent IV surfaces using VAEs and SDE models.
problem Creating arbitrage-free IV surfaces from historical data.
method Combining VAEs with SDE models for parameter distribution, sampling, and decoding.
result Superior out-of-sample performance of the refined VAE model.
The study classifies compact Cauchy horizons in vacuum spacetimes.
problem Classifying compact Cauchy horizons in vacuum spacetimes.
method Complete classification theorem based on topology and null generators.
result Different cases of compact Cauchy horizons with specific manifolds and spacetime properties.
Proposes a new estimator for weak instrumental variables in panel data models.
problem Weak instrumental variables due to ignored nonlinearities in panel data.
method Triangular simultaneous equation model with a nonlinear reduced form equation and a control function approach using Super Learner.
result The proposed SLCF estimator is consistent and asymptotically normal, achieving a parametric rate of convergence.
ZNet learns instrumental representations from covariates for causal inference.
problem Lack of valid instruments in observational studies.
method Representation learning approach that constructs instrumental representations from observed covariates.
result ZNet enables IV-based estimation without explicit instruments.
DFIV uses deep neural nets to learn nonlinear features in IV regression.
problem Learning causal relationships from observational data with nonlinear interactions.
method DFIV trains deep neural nets to define nonlinear features on instruments and treatments, alternating training to compose stages 1 and 2.
result DFIV outperforms state-of-the-art methods on IV benchmarks and off-policy policy evaluation.
New methods correct for time dependencies in IV regression for time series data.
problem Inferring causal effects from time series data with unobserved confounders.
method Proposes new methods for consistent estimation of causal effects in time series models using nuisance covariates and graph marginalization.
result Identifies and corrects for dependencies in the past, leading to consistent estimation of causal effects.
A new Riemannian metric on curve spaces is complete and smooth.
problem Defining a complete metric on the space of embedded curves.
method Proposed a new Riemannian metric and proved its completeness.
result The proposed metric is complete in multiple senses.
Paper introduces a new IV regression method for mixed-frequency data.
problem Estimating high-dimensional slope parameters in mixed-frequency data.
method Tikhonov-regularized estimator for high-dimensional linear IV regression.
result High-dimensional slope parameter can be accurately estimated using a low-frequency instrumental variable.
Simplifies IV regression for high-dimensional instruments.
problem Nonlinear instrumental variable regression with high-dimensional instruments.
method Combines kernelized IV methods with an adaptive regression algorithm.
result Faster convergence and adaptability to feature dimensionality.
Study on short-term behavior of ATM-IV for jump-diffusion model.
problem Analyzing the short-time behavior of ATM-IV for a specific stochastic volatility model.
method Used Malliavin Calculus techniques to derive expressions for ATM-IV level and skew.
result Short-time behavior of ATM-IV level is consistent for all pure-jump Lévy processes.
A new algorithm uses IVs to learn optimal policies from observational data.
problem Learning optimal policies from unobserved variable confounded data.
method IV-aided Value Iteration (IVVI) algorithm based on conditional moment restrictions.
result First provably efficient algorithm for instrument-aided offline RL.
Class I CR manifolds have initial G-structure a certain 4-dimensional subgroup of GL_3(C). Class II CR manifolds have initial G-structure a certain 10-dimensional subgroup of GL_4(C). Class III-1 CR manifolds have initial G-structure a certain 10-dimensional subgroup of GL_5(C). Class III-2 CR manifolds have initial G-…
The six nondegeneracy conditions of geometric nature that are satisfied by the only six possibly existing nondegenerate general classes I, II, III-1, III-2, IV-1, IV-2 of 5-dimensional CR manifolds are shown to be readable instantaneously from their elementarily normalized respective defining graphed equations, without…
Proposes RDIV for IV estimation avoiding limitations of existing methods.
problem Nonparametric estimation of IV regressions with practical limitations.
method Tikhonov-regularized DeepIV regression with model selection.
result Matches state-of-the-art convergence rate and provides rigorous guarantees.