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.
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.
Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks…
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.
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.
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.
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.
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.
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.
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.
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…
We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music. Our solution employs a single mapping function g, which, applied to a mixed sample, recovers the underlying instrum…
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.
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.
In this paper, we learn disentangled representations of timbre and pitch for musical instrument sounds. We adapt a framework based on variational autoencoders with Gaussian mixture latent distributions. Specifically, we use two separate encoders to learn distinct latent spaces for timbre and pitch, which form Gaussian …
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.
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.
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.
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.
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.
Study shows annotation instrument design affects model performance in hate speech detection.
problem Impact of annotation instrument design on model performance in hate speech detection.
method Collected annotations from five experimental conditions of an annotation instrument, fine-tuned BERT models on each dataset, evaluated performance on holdout portion.
result Significant differences in model performance and annotations across conditions.
Estimates linear model from noisy covariates and instruments using spectral regularization.
problem Estimating a linear model from many noisy covariates and instruments.
method Two-stage least squares with spectral regularization of canonical correlations.
result Upper and lower bounds on estimation error, proving optimality of the method with noisy data.
Recidivism prediction instruments (RPI's) provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy c…
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.
New method exploits independence in instrumental variable models for better causal inference.
problem Identify causal functions in the presence of unobserved confounders.
method HSIC-X method that exploits independence between response, hidden confounders, and instruments.
result The method provides better finite sample results and is invariant to distributional 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.
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.
StructureBoost improves gradient boosting for complex categorical variables efficiently.
problem Efficiently handling complex categorical variables with known structure.
method Two methods to overcome computational obstacles in SCDT enumeration for structured categorical variables.
result StructureBoost outperforms existing packages on complex categorical problems.
Categorical bundles provide a natural framework for gauge theories involving multiple gauge groups. Unlike the case of traditional bundles there are distinct notions of triviality, and hence also of local triviality, for categorical bundles. We study categorical principal bundles that are product bundles in the categor…
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.
Improves financial instrument pricing using neural networks.
problem Financial instrument pricing within Black-Karasinski model.
method Enhances path-integral approximation with neural networks.
result Demonstrates superior outcomes for multiple calibrations.
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. Proposes a method to estimate treatment effects using instruments.
problem Estimating treatment effects from observational data is challenging when unconfoundedness is violated.
method Leverages instruments to estimate bounds on conditional average treatment effect (CATE) through a mapping to a discrete representation space and a two-step procedure.
result Demonstrates theoretical validity and reduced estimation variance in finite-sample settings.
Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times…
The definition of deposit substitutes in Philippine tax law fails to consider the maturity of a debt instrument. This makes it possible for long-term bonds to be considered as deposit substitutes if they meet the 20-lender rule, taxable at 20% final tax. However, long-term debt instruments cannot realistically function…
Bayesian model improves categorization of explosions from sparse data.
problem Challenges in categorizing explosions from limited data.
method Bayesian update to Event Categorization Matrix model with Bayesian Decision Theory.
result Consistent gains in overall accuracy and lower false negative rates.
This work aims to examine one of the cornerstone problems of Musical Instrument Retrieval (MIR), in particular, instrument classification. IRMAS (Instrument recognition in Musical Audio Signals) data set is chosen for this purpose. The data includes musical clips recorded from various sources in the last century, thus …
In complete markets, there are risky assets and a riskless asset. It is assumed that the riskless asset and the risky asset are traded continuously in time and that the market is frictionless. In this paper, we propose a new method for hedging derivatives assuming that a hedger should not always rely on trading existin…
Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns …
Deep learning for integrating diverse clinical measurements.
problem Combining data from different measurement instruments in longitudinal clinical registries.
method Domain adaptation using deep learning for mapping items from different instruments.
result Domain adaptation can recover latent trajectories even with limited data and misalignment.
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.
UNTIE learns representations of coupled categorical data.
problem Challenges in learning from unlabeled categorical data with complex couplings.
method UNTIE approach for unsupervised representation learning of heterogeneous couplings.
result UNTIE significantly improves categorical data representations on 25 diverse datasets.
Paper introduces Categorical Normalizing Flows for better handling of categorical data.
problem Limited application of normalizing flows on categorical data due to lack of intrinsic order.
method Categorical Normalizing Flows use continuous transformations to model latent relations in categorical data, optimizing both continuous representation and model likelihood.
result GraphCNF, a permutation-invariant generative model, outperforms state-of-the-art on molecule generation.
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.
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 proposes a method to reduce complexity in GLMs with categorical predictors.
problem Wasteful, hard-to-interpret, and prone to overfitting of traditional one-hot encoding for high-cardinality categorical predictors.
method Clustering categories of categorical predictors through a numerical method that preserves or improves accuracy while reducing the number of coefficients.
result Clustering categories of categorical predictors reduces complexity substantially without harming accuracy.
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.
Generative adversarial network improves audio inpainting for long gaps.
problem Generating missing audio content in long-range gaps using WGAN.
method Proposed WGAN architecture with short-range and long-range neighboring borders.
result The proposed model outperforms classical WGAN in reconstructing high-frequency content.