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.
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.
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.
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.
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.
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.
DFIV uses deep features for IV regression, achieving optimal rates.
problem Optimal IV regression with deep features for complex target functions.
method Two-stage approach: deep feature learning followed by IV regression.
result DFIV achieves minimax optimal learning rate under certain conditions.
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.
We present a novel algorithm for non-linear instrumental variable (IV) regression, DualIV, which simplifies traditional two-stage methods via a dual formulation. Inspired by problems in stochastic programming, we show that two-stage procedures for non-linear IV regression can be reformulated as a convex-concave saddle-…
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.
Method constructs nonparametric prediction intervals with finite-sample guarantees.
problem Nonparametric instrumental variable regression with finite-sample coverage.
method Conformal inference framework applied to NPIV, combining with various estimators.
result Distribution-free, finite-sample coverage over chosen IV shifts.
New method avoids IV limitations for flexible estimation.
problem Nonparametric estimation of IV regressions with multiple solutions.
method Minimax penalized estimator avoiding identification and closedness conditions.
result Strong L2 convergence rate without closedness condition. 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.
New method tackles endogeneity in online learning with improved regret bounds.
problem Endogeneity in real data due to omitted variables, strategic behaviors, etc.
method O2SLS (Online Two-Stage Least Squares) for Instrumental Variable (IV) regression.
result O2SLS achieves identification and oracle regret bounds for stochastic online learning.
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.
New algorithm for nonparametric IV regression using stochastic gradients.
problem Identifying causal effects in the presence of unobservable confounders.
method Functional stochastic gradient descent for NPIV regression.
result Superior stability and competitive performance compared to existing methods.
DML-CMR estimator reduces bias in CMR problems using deep neural networks.
problem Solving conditional moment restrictions with deep neural networks.
method Double/debiased machine learning framework for unbiased estimation.
result Achieves minimax optimal convergence rate of O(N−1/2). Instrumental variable (IV) regression is a strategy for learning causal relationships in observational data. If measurements of input X and output Y are confounded, the causal relationship can nonetheless be identified if an instrumental variable Z is available that influences X directly, but is conditionally independe…
Transformers can handle endogeneity in linear regression using IV methods.
problem Endogeneity in in-context linear regression models.
method Transformer architecture with gradient-based bi-level optimization and in-context pretraining.
result Transformers provide more robust predictions and estimates than 2SLS in endogenous scenarios.
Proposes a robust IV estimator using optimal transport for corrupted or adversarial data.
problem Lack of robustness in traditional IV estimators for corrupted or adversarial data.
method Integrates data-derivative information through optimal transport to address geometric aspects of data.
result Improves robustness against data corruption and adversarial attacks.
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.
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.
This paper presents the asymptotic behavior of a linear instrumental variables (IV) estimator that uses a ridge regression penalty. The regularization tuning parameter is selected empirically by splitting the observed data into training and test samples. Conditional on the tuning parameter, the training sample creates …
New method uses data augmentation to improve causal effect estimation.
problem Improving causal effect estimation in the presence of hidden confounders.
method Introduces IV-like regression and data augmentation techniques.
result Data augmentation can simulate worst-case scenarios for causal estimation.
In this work, we design a machine learning based method, online adaptive primal support vector regression (SVR), to model the implied volatility surface (IVS). The algorithm proposed is the first derivation and implementation of an online primal kernel SVR. It features enhancements that allow efficient online adaptive …
Causal effect estimation relies on separating the variation in the outcome into parts due to the treatment and due to the confounders. To achieve this separation, practitioners often use external sources of randomness that only influence the treatment called instrumental variables (IVs). We study variables constructed …
Unified framework connects credit risk metrics with information theory.
problem Disconnection between industry-standard metrics and statistical theory.
method Unified information-theoretic framework, proving IV equals PSI, deriving standard errors, formalizing trade-off, automated binning with XGBoost.
result Unified framework connects IV and PSI, providing statistical foundation for metrics.
Paper introduces EnCounteR for estimating causal effects using encouragement data.
problem Challenges in estimating causal effects due to incomplete randomization and limited encouragement data.
method Introduces a generalized IV estimator, EnCounteR, leveraging both observational and encouragement data.
result Demonstrates superior performance of EnCounteR over existing methods.
Paper uses VAEs to control IVS features for financial modeling.
problem Generating realistic IVSs with desired characteristics.
method Variational autoencoder architecture with controllable latent variables.
result Controlled generation of IVSs with specified features.
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.
This paper provides a link between causal inference and machine learning techniques - specifically, Classification and Regression Trees (CART) - in observational studies where the receipt of the treatment is not randomized, but the assignment to the treatment can be assumed to be randomized (irregular assignment mechan…
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.
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.
Paper optimizes estimation of quadratic functionals in nonparametric IV models.
problem Optimal estimation of a nonlinear functional in ill-posed inverse regression.
method Adaptive, minimax estimation using leave-one-out, sieve NPIV estimator with data-driven sieve dimension selection.
result Adaptive estimator achieves minimax optimal rate in various ill-posed cases.
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.
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…
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.
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.
New algorithms for IV regression with streaming data, avoiding matrix inversions.
problem Instrumental variable regression with streaming data.
method Viewing IV regression as a stochastic optimization problem, developing algorithms that avoid matrix inversions and mini-batches.
result Rates of convergence of order O(logT/T) and O(1/T1−ι) for linear models. 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…
Study on estimating causal effects with limited data and multiple environments.
problem Estimating causal effects under hidden confounding with unpaired data and sparse effects.
method Instrumental variable (IV) regression with cross-fold sample splitting and ℓ1-regularized estimation. result Proposed GMM-type estimator is consistent as the number of environments grows.
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.
Simplified kernel ridge regression with a conservation law.
problem Understanding the test risk and generalization of kernel ridge regression.
method Identification of a conservation law that limits KRR's learning ability, leading to simplified expressions for test risk.
result Transparency in test risk expressions through the conserved quantity in the kernel eigenbasis.