A framework uses proxies to prioritize treatment without estimating causal effects.
problem Prioritizing treatment when causal effects are hard to estimate.
method Decision-focused framework identifying conditions for proxy usefulness.
result Proxies can recover correct effect ordering under specific conditions.
Single proxy variable helps estimate causal effects from confounders.
problem Estimating causal effects from treatment to outcome when unobserved confounders are present.
method Assumes a single, potentially multi-dimensional proxy variable of the unobserved confounder and a known mechanism generating the proxy from the confounder. Proves causal effects are identifiable under completeness assumption.
result Causal effects are identifiable under SPICE assumption.
New method handles many noisy proxy controls for causal inference.
problem Causal inference with many noisy proxy controls and unknown confounders.
method Linear models with rank-restricted and sparse nuisance parameters, penalization methods.
result Estimators achieve better performance in high dimensions, especially with many proxies.
Estimates proxy-based inference adjustments for distribution shifts.
problem Imperfect proxy data leads to biased inference.
method Empirical calibration of proxy-primary metric discrepancy as a random effect.
result Empowers inference without individual-level response data.
New method recovers latent confounders from high-dimensional proxy variables.
problem Detecting latent confounders from high-dimensional proxy variables.
method Proxy Confounder Factorization (PCF) framework using ICA-PCF and GD-PCF.
result ICA-PCF recovers confounders with high correlation and low error in synthetic and real-world data.
Estimates causal effect using proxies in multi-domain settings.
problem Estimating causal effect in settings with unobserved confounders across domains.
method Proposes estimation techniques using proxy variables for discrete or categorical data.
result Proves identifiability and consistency of causal effect estimation.
Kernel methods estimate causal effects with a single proxy for deterministic confounders.
problem Estimating causal effects with a single proxy for an unobserved confounder.
method Two kernel-based methods: two-stage regression and maximum moment restriction.
result Both kernel methods can consistently estimate the causal effect.
Proposes a method to create robust linear models with noisy proxies of unobserved variables.
problem Learning robust linear models to handle interventions on unobserved variables with noisy proxies.
method Regularization term that balances in-distribution performance and robustness to interventions.
result Single proxy can create prediction optimal estimators under interventions of bounded strength.
Q-NETs use neural networks to estimate integrals of low-dimensional functions efficiently.
problem Estimating integrals of multidimensional functions with costly evaluations.
method Fixed neural networks (Q-NETs) that operate on proxy function parameters to calculate exact integrals over subsets of dimensions.
result Q-NETs can calculate integrals over any subset of dimensions without resampling or retraining the proxy.
Develops a method to estimate treatment effects using noisy proxies over time.
problem Estimating individualized treatment effects from noisy proxies of confounders.
method Deconfounding Temporal Autoencoder (DTA) combining autoencoder and causal regularization.
result Improves treatment effect estimates by leveraging noisy proxies and learning hidden confounders.
New method combines strengths of two PCL approaches without density ratio estimation.
problem Estimating causal functions in Proxy Causal Learning with unobserved confounders and proxies.
method Kernel-based doubly robust estimators combining treatment and outcome bridges, density ratio-free.
result Outperforms existing methods on PCL benchmarks, including a prior doubly robust method.
Bayesian method estimates causal effects with proxy networks.
problem Estimating causal effects with only proxy measurements of a latent interference network.
method Structural causal model with Block Gibbs sampler and Locally Informed Proposals.
result Accurately estimates causal effects even with noisy proxy networks.
Study semi-supervised learning with noisy proxy covariates, deriving bounds and showing gains.
problem Learning from noisy proxy covariates with scarce labels.
method Two-stage estimator learning kernel eigenfeatures from all proxy covariates and fitting a ridge predictor on labeled data.
result Finite sample bounds show fast labeled sample rates and consistent gains over supervised and semi-supervised baselines.
Algorithm identifies best arm with biased proxy and selective ground truth audits.
problem Fixed-confidence best-arm identification with biased proxy and selective ground truth.
method Propensity-weighted estimator and adaptive auditing algorithm.
result Plug-in Neyman rule achieves near-oracle audit efficiency.
Granger causality analysis, as one of the most popular time series causality methods, has been widely used in the economics, neuroscience. However, unobserved confounders is a fundamental problem in the observational studies, which is still not solved for the non-linear Granger causality. The application works often de…
Predictive analytics is increasingly used to guide decision-making in many applications. However, in practice, we often have limited data on the true predictive task of interest, and must instead rely on more abundant data on a closely-related proxy predictive task. For example, e-commerce platforms use abundant custom…
DFPV improves PCL for confounded bandit policy evaluation.
problem Estimating causal effects in confounded settings with high-dimensional data.
method Deep feature proxy variable method (DFPV) for high-dimensional, nonlinear relationships.
result DFPV outperforms state-of-the-art methods on synthetic benchmarks and confounded bandit problems.
Extends causal inference to hidden mediators with proxies.
problem Identifying causal effects with hidden mediators and error-prone proxies.
method Established causal hidden mediation analysis and hidden front-door criterion.
result Identification of population intervention indirect effect possible with hidden mediators.
New framework for choosing optimal proxy metrics from past experiments.
problem Difficult to measure long-term treatment effects in experiments.
method Statistical framework to define and construct optimal proxy metrics.
result Optimal proxy metric depends on experiment's sample size.
Estimates causal effects with selection bias and confounding using regression.
problem Estimating causal effects in presence of selection bias and confounding.
method Two-step regression estimator (TSR) that corrects for selection bias and accounts for confounding.
result TSR estimator reduces variance and is validated in simulations.
New method improves treatment effect estimation using autoencoders and causal bridge.
problem Inferring causal effects with unobserved confounders.
method Coupling autoencoder with causal bridge to estimate treatment effects.
result Improves accuracy of treatment effect estimates.
New method identifies proxies for causal effects on multiple outcomes.
problem Estimating causal effects in scenarios with multiple outcomes and treatments.
method Causal discovery method leveraging multiple outcomes as proxies for each treatment effect.
result Parallel studies of multiple outcomes can assist in causal identification.
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units.…
Proposes P-learner for estimating treatment effects with proxy variables.
problem Estimating treatment effect heterogeneity in settings with unverifiable exchangeability.
method Two-stage loss function for learning heterogeneous treatment effects with proxy variables.
result P-learner satisfies an oracle bound on estimated error.
PROBE optimizes best-arm identification with cheap proxies, improving sample complexity.
problem Fixed-confidence best-arm identification with costly rewards and correlated cheap proxies.
method PROBE uses control-variate adjustment and phase elimination to learn residual variance online.
result PROBE achieves oracle sample complexity up to a constant factor and additive calibration cost.
Proxy methods adapt to distribution shifts without explicitly modeling latent confounders.
problem Adapting to distribution shifts under latent variable confounding.
method Proximal causal learning, two-stage kernel estimation.
result Proxy methods outperform other methods in adapting to complex distribution shifts.
New method removes unwanted information from representations efficiently.
problem Learning representations that are uninformative about a target variable.
method Adversarial training with a novel proxy metric for mutual information, leading to an analytically computable approximation.
result Our method effectively removes unwanted information with limited time budget.
Estimates disease prevalence using non-ignorable missing data in health surveys.
problem Estimating disease prevalence in non-representative samples with non-ignorable missing data.
method Connects auxiliary proxy variable framework to label shift setting, uses high-dimensional covariates without generative models.
result Fails to account for non-ignorable missingness can lead to significant misestimations.
New method identifies latent treatment effects from proxy models.
problem Identifying heterogeneous treatment effects under unobserved confounding.
method Compressed observable operator and spectral analysis of treatment effects.
result Eigenvalues of the operator represent latent treatment effects.
One of the biggest bottlenecks in a machine learning workflow is waiting for models to train. Depending on the available computing resources, it can take days to weeks to train a neural network on a large dataset with many classes such as ImageNet. For researchers experimenting with new algorithmic approaches, this is …
Detects harmful shifts without labels for model performance.
problem Detecting distribution shifts without access to labels.
method Uses a proxy derived from predictions of an error estimator.
result High power and false alarm control under various shifts.
The study examines fairness metrics in noisy covariate settings, providing theoretical guarantees.
problem Fairness measurement under noisy covariate information.
method Theoretical analysis of group fairness metrics using proxies for covariates.
result Characterization of weaker conditions for accurate fairness evaluation.
Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are factors that affect both an intervention and its outcome. Most previous work relies on the unconfoundedness assumption, which posits that all th…
Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific…
ProxySHAP approximates Shapley and Banzhaf interactions efficiently.
problem Efficient estimation of complex machine learning interactions.
method ProxySHAP combines tree-based proxy models with residual correction.
result ProxySHAP achieves state-of-the-art interaction approximation quality.
New method identifies causal effects with categorical unobserved confounders.
problem Estimating causal effects in the presence of unobserved confounders.
method Mixture learning and tensor decomposition for consistent estimation.
result Causal effects are identifiable with categorical unobserved confounders under suitable conditions.
A new framework improves VaR recalibration by balancing reliance on imperfect volatility proxies.
problem How to balance reliance on imperfect volatility proxies in one-sided VaR recalibration.
method Proxy-reliance control framework that interpolates between constant-shift and proxy-scaled corrections.
result Lower or intermediate proxy reliance can outperform fully proxy-scaled recalibration in stressed left-tail VaR control.
Discovering causal relations is fundamental to reasoning and intelligence. In particular, observational causal discovery algorithms estimate the cause-effect relation between two random entities X and Y, given n samples from P(X,Y). In this paper, we develop a framework to estimate the cause-effect relation bet…
New method improves reliability of selecting individuals based on predicted treatment effects.
problem Reliability of selecting individuals based on predicted conditional average treatment effects (CATE) is unreliable.
method Denoised Conformal Alignment, combining proxy errors, variance estimation, and Benjamini-Hochberg selection.
result Significantly improved power in selecting individuals while maintaining false discovery rate control.
Study accelerates NAS research with a large dataset of ZC proxies.
problem Speeding up neural architecture search with ZC proxies.
method Created NAS-Bench-Suite, evaluated 13 ZC proxies across 28 tasks, and provided a unified codebase.
result ZC proxies capture substantial complementary information and can improve NAS algorithm performance.
In this paper I show how reliable estimates of the Value of a Statistical Life (VSL) can be obtained using cross sectional data using Garen's instrumental variable (IV) approach. The increase in the range confidence intervals due to the IV setup can be reduced by a factor of 3 by using a proxy to risk attitude. In orde…
Unified framework for analyzing neural networks trained by gradient descent.
problem Lack of generalizable guarantees for neural networks trained by gradient descent.
method Proxy convexity and proxy Polyak-Lojasiewicz inequalities.
result Unified guarantees for neural networks trained by gradient descent.
Estimates model performance under distribution shift using domain-invariant predictors.
problem Poor performance of models on test distributions different from training distributions.
method Uses domain-invariant predictors as a proxy for unknown target labels.
result Shows that the complexity of latent representations influences target risk.
Activists align with large fund preferences for success.
problem Aligning with large fund preferences increases activist success.
method Analyzed previous proxy voting behavior to estimate preferences and correlated them with activist success.
result Campaigns with higher alignment receive more votes and are more successful.
Study tackles OPE in confounded settings, estimating policy value from proxies.
problem Difficulty in OPE due to unobserved confounders in infinite-horizon RL.
method Two-stage approach: estimating stationary distribution ratios and combining optimal balancing.
result Policy value can be identified from off-policy data with proxies and latent variable model.
PPI uses proxy data to improve inference from limited labels across related tasks.
problem Statistical inference with limited labels across multiple related tasks.
method Prediction-powered inference framework that uses cross-task recalibration to improve power and accuracy.
result Cross-task recalibration can substantially reduce confidence interval widths when labels are scarce.
PPAT uses predictions to improve risk estimation in active testing.
problem Exploiting informative predictions from black-box models for efficient risk estimation.
method Combines LURE estimator with prediction-powered control variate.
result PPAT outperforms existing methods in risk estimation and uncertainty quantification.
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.