Bayesian nonparametric machine learning improves instrumental variable inference.
problem Estimating causal effects with nonlinear relationships.
method Bayesian Additive Regression Trees (BART) for estimating functions and Dirichlet Process mixtures for error terms.
result Dramatic improvements in inference with nonlinear data, no manual tuning required.
Estimates long-term effects using past experiments as instruments with many weak instruments.
problem Estimating long-term causal effects with limited short-term outcomes and many weak instruments.
method Nonparametric instrumental variable inference with many weak instruments, using past experiments as instruments.
result Automatic debiased machine learning estimators for linear functionals of the structural function and its minimum-norm projection are efficient in the many-weak-instruments regime.
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.
Kernel method improves instrumental variable regression rates.
problem Nonparametric instrumental variable regression with weak instruments.
method Kernel-based two-stage least-squares method, strong L2 convergence analysis. result Minimax optimal rates for instrumental regression under standard assumptions.
New method for causal inference with observed covariates improves learning rates.
problem Causal inference with observed covariates in nonparametric instrumental variable regression.
method Introduces novel Fourier measure for partial smoothing and adapts kernel lengthscales for anisotropic smoothness.
result Upper and lower learning rates for KIV-O show interpolation between NPIV and NPR rates.
Perturbation theory improves nonparametric instrumental variable estimation accuracy.
problem Improving nonparametric instrumental variable estimation accuracy in high-dimensional settings.
method Perturbative approach based on physics perturbation theory, extending kernel ridge methods with higher-order corrections.
result First-order perturbative corrections reduce prediction error by up to 99% in high-dimensional ill-defined cases.
New methods for estimating complex causal effects in econometrics.
problem Estimating causal parameters in short panel data models using nested nonparametric instrumental variable regression.
method Introducing techniques to limit ill-posedness in nested NPIV, providing explicit mean square rates and efficient inference.
result Explicit mean square rates for nested NPIV and efficient inference for causal parameters.
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…
Paper develops PGMM framework for debiased inference on nonparametric IV estimators.
problem Automatic debiased inference on nonparametric IV functionals.
method Penalized GMM (PGMM) framework for functionals of IV estimators.
result PGMM-based debiased estimator performs well, achieving near-nominal coverage.
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 method for debiased inference without assuming exact solutions in inverse problems.
problem Dealing with inverse problems where exact solutions may not exist.
method Nonparametric instrumental variable analysis without structural equations.
result Valid inference on functionals of inverse problems without assuming exact solutions.
Develops methods to identify and estimate causal effects with instrumental variables.
problem Causal inference with confounded treatment assignment and unobserved variables.
method General nonparametric causal framework, debiased machine learning, semiparametric theory.
result Consistent and asymptotically normal estimators for average treatment effect.
Unified analysis of neural networks in NPIV using 2SLS and MFLD.
problem Global convergence of neural networks in NPIV.
method Lifted perspective through MFLD, penalty gradient approach for bilevel optimization.
result First global convergence result of neural networks for 2SLS in NPIV.
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. A new method learns outcome-aware spectral features for causal effect estimation.
problem Estimation of causal effects in the presence of hidden confounders.
method Augmented Spectral Feature Learning framework that minimizes a contrastive loss derived from an augmented operator incorporating outcome information.
result Our method remains effective even under spectral misalignment.
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.
New methods for estimating and inferring nonparametric structural functions and elasticities.
problem Estimating and inferring nonparametric structural functions and their derivatives.
method Data-driven sieve dimension choice and uniform confidence bands construction.
result Optimal estimation and inference procedures with minimax rates of convergence.
Study on Q-function estimation for continuous state-action MDPs, deriving rates and conditions.
problem Estimating Q-function in off-policy evaluation for continuous state-action Markov decision processes. method Reformulated as nonparametric instrumental variables (NPIV) problem, derived minimax lower bounds, proposed sieve two-stage least squares estimator.
result First minimax lower bounds for Q-function and its derivatives in sup-norm and L2-norm, same as classical nonparametric regression. 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.
New adaptive test for NPIV models controls size and has superior power.
problem Testing inequality and equality restrictions in nonparametric IV models.
method Adaptive hypothesis test based on modified leave-one-out sample quadratic distance.
result Adaptive test attains the adaptive minimax rate of testing in L2. This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via …
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
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.
Paper proposes CIV estimator for categorical instruments in small sample settings.
problem Estimation with categorical instruments in settings with few observations per category.
method CIV estimator leveraging regularization assumption for latent categorical variable.
result CIV estimator is asymptotically normal, efficient, and semiparametrically efficient under homoskedasticity.
Researchers develop methods for causal inference with imperfect instrumental variables.
problem Quantifying cause and effect relationships with imperfect instrumental variables.
method Established a quantitative relationship between violations of instrumental inequalities and minimal measurement dependence, providing adapted inequalities valid in the presence of relaxed measurement dependence.
result Adapted inequalities for average causal effect in instrumental scenarios with binary outcomes, addressing violations of instrumental inequalities.
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions. If hidden common causes between treatment X and outcome Y cannot be blocked by other measurements, one possibility is to use an instrumental variable. In principle, it is possible under some…
TSCI estimates treatment effects using machine learning and data-adaptive methods for invalid instruments.
problem Estimating treatment effects with invalid instruments.
method Two-stage algorithm: first stage uses machine learning for nonlinearities, second stage selects and projects out instrument violations.
result Effective treatment effect estimation even with invalid 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 method uses few instruments to estimate complex causal effects.
problem Estimating causal effects with limited instruments in high-dimensional settings.
method Sequentially selects and combines instruments to estimate the treatment effect.
result Can reliably recover the treatment effect's projection onto the instrumented subspace.
New method for personalized pricing using invalid instrumental variables.
problem Personalized pricing under endogeneity with limited standard methods.
method PRINT method for continuous treatment, solving conditional moment restrictions.
result Established optimal pricing strategy under endogeneity with invalid instrumental variables.
Modern data analysis depends increasingly on estimating models via flexible high-dimensional or nonparametric machine learning methods, where the identification of structural parameters is often challenging and untestable. In linear settings, this identification hinges on the completeness condition, which requires the …
This paper theoretically explains and validates a deep neural network approach to IV estimation.
problem Endogeneity issues in empirical applications, especially in the presence of omitted variables, measurement error, or simultaneous causality.
method A two-stage estimator using deep neural networks in a linear instrumental variables model, with a latent structural assumption on the reduced form equation.
result The second-stage estimator achieves the semiparametric efficiency bound, with a smaller estimation error and requiring weaker conditions on the smoothness of optimal instruments.
Proposes TSCI method to infer causal effects with weak or invalid instruments using machine learning.
problem Causal inference with weak or invalid instrumental variables.
method Two-stage curvature identification (TSCI) using machine learning.
result Asymptotically unbiased and Gaussian estimator for causal effects.
Paper develops methods for inference on time series data using neural networks and sieves.
problem Inference on time series data with nonparametric conditional moment restrictions.
method GN-QLR based inference using general nonlinear sieves and multilayer neural networks.
result Optimally weighted GN-QLR statistic is asymptotically Chi-square distributed.
The paper addresses statistical estimation in MDPs with confounders using instrumental variables.
problem Statistical estimation of value functions in MDPs with unobservable confounders.
method Two-stage estimator based on instrumental variables for confounded linear MDPs.
result Established statistical properties of the two-stage estimator, including error bounds and asymptotic normality.
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.
Valid causal inference with invalid instruments using majority or modal valid relationships.
problem Estimating causal effects in the presence of unobserved confounding and invalid instruments.
method Ensemble of instrumental variable estimators to estimate the modal prediction, achieving accurate estimates of conditional average treatment effects.
result Valid causal inference can be achieved with a majority or modal valid instrument-response relationship.
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 paper identifies causal effects in latent variable models using higher-order cumulants.
problem Challenges in identifying causal effects in latent variable models with latent confounders.
method Using higher-order cumulants, the paper addresses two challenging setups: a single proxy variable and underspecified instrumental variables.
result Causal effects are identifiable with a single proxy or instrument.
GIV methodology extends instrumental variable estimation for high-dimensional data.
problem Estimating structural parameters in high-dimensional models with endogeneity and latent factors.
method Extends GIV methodology to large N and T, treats factors and loadings as unknown, and uses additional instruments for efficiency.
result Efficiency gains and negligible sampling errors in estimated instrument and factors.
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.
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.
The paper uses graph learning to detect valid instruments in high-dimensional data for house pricing.
problem Endogeneity bias and invalid instrument validation in high-dimensional data.
method Merge variable selection algorithms and probabilistic graphs to estimate house prices and causal structure.
result Efficient data-driven instrument selection and invalid instrument purge in high-dimensional data.
Aggregation challenges causal interpretation of IV estimators.
problem Aggregation of fine-grained components into an aggregate treatment variable.
method Characterization of conditions for identifying aggregate causal effects.
result Standard IV estimators cannot identify aggregate causal effects due to ambiguous dependencies.
Bayesian method for robust causal inference using many-dimensional instrumental variables.
problem Intractable model space and uncertainty in selecting valid instrumental variables.
method Bayesian model averaging over promising instrumental variable models with weaker assumptions.
result Efficient and robust causal effect estimation in many-dimensional data.
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.
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.
A new boosting method corrects endogeneity bias in instrumental variable regression.
problem Endogeneity bias in instrumental variable regression.
method Causal Gradient Boosting (boostIV) that builds on gradient boosting algorithm.
result boostIV is consistent and performs well in finite samples compared to other methods.