A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study evaluates machine learning for predicting treatment effects in observational studies.
problem Challenges in measuring treatment effects due to confounding bias in observational studies.
method Simulated two scenarios with and without confounding, using linear and non-linear relationships. Used machine learning models (linear regression, lasso regression, random forest) to predict counterfactuals and treatment effects.
result Machine learning models perform well under linearity but poorly under non-linearity, even in the presence of confounding.
We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We introduce σ-connection graphs (σ-CG), a new class of mixed graphs (containing undirected, bidirected…
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…
Method learns causal effects from multiple interventions in presence of unobserved confounders.
problem Disentangling causal effects from sets of interventions in the presence of unobserved confounders.
method Non-linear structural causal models with additive, multivariate Gaussian noise; algorithm that learns causal model parameters by pooling data from different regimes and maximizing combined likelihood.
result Identification proofs demonstrate that causal effects of single interventions can be learned from sets of interventions, even with unobserved confounders.
This paper studies semiparametric contextual bandits, a generalization of the linear stochastic bandit problem where the reward for an action is modeled as a linear function of known action features confounded by an non-linear action-independent term. We design new algorithms that achieve O~(dT) regret …
Causal approaches to fairness have seen substantial recent interest, both from the machine learning community and from wider parties interested in ethical prediction algorithms. In no small part, this has been due to the fact that causal models allow one to simultaneously leverage data and expert knowledge to remove di…
We prove the main rules of causal calculus (also called do-calculus) for i/o structural causal models (ioSCMs), a generalization of a recently proposed general class of non-/linear structural causal models that allow for cycles, latent confounders and arbitrary probability distributions. We also generalize adjustment c…
I argue that regularizing terms in standard regression methods not only help against overfitting finite data, but sometimes also yield better causal models in the infinite sample regime. I first consider a multi-dimensional variable linearly influencing a target variable with some multi-dimensional unobserved common ca…
Extracts invariant features to predict Y without confounding by Z, using conditional independence and optimal transport.
problem Extracting invariant features to predict Y without confounding by Z, a response variable influenced by unknown confounders Z.
method Develops a methodology penalizing statistical dependence between feature and confounders conditioned on Y, using the Optimal Transport Barycenter Problem.
result The method extracts invariant features in the Gaussian case, equivalent to penalizing dependence between feature and conditional random variable Z_Y.
High-dimensional data acquired from biological experiments such as next generation sequencing are subject to a number of confounding effects. These effects include both technical effects, such as variation across batches from instrument noise or sample processing, or institution-specific differences in sample acquisiti…
MOCA uses modular attention to estimate causal effects from complex data.
problem Estimating causal effects from observational data with complex, non-linear, and high-dimensional treatment and outcome mechanisms.
method MOCA is a transformer-based framework that separates treatment and outcome modeling through modular design and one-way attention mechanism, with cutting-feedback to prevent outcome influence on treatment representations.
result MOCA outperforms classical estimators and machine learning approaches across various simulated and real-world scenarios.
Statistical learning on biological data can be challenging due to confounding variables in sample collection and processing. Confounders can cause models to generalize poorly and result in inaccurate prediction performance metrics if models are not validated thoroughly. In this paper, we propose methods to control for …
Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assumptions have focused on estimation of effects for a single treatment. In this work, we construct techniques for estimation with multiple treat…
Causal discovery from data affected by latent confounders is an important and difficult challenge. Causal functional model-based approaches have not been used to present variables whose relationships are affected by latent confounders, while some constraint-based methods can present them. This paper proposes a causal f…
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…
Valid causal inference with unobserved confounding in high-dimensional settings.
problem Estimating causal effects with unobserved confounders in high-dimensional data.
method Proposes methods to estimate causal effects with valid confidence intervals in the presence of unobserved confounders and high-dimensional nuisance models.
result Valid semiparametric inference can be obtained with unobserved confounding, and uncertainty intervals are proposed.
problem Machine learning models can be misled by hidden confounders, leading to unreliable predictions.
method Develops a nonlinear spectral deconfounding framework for gradient boosting that modifies boosting dynamics to slow down in confounding-aligned directions.
result Spectrally deconfounded boosting improves estimation of the target function under hidden confounding and is more scalable.
We propose a method for inferring the existence of a latent common cause ('confounder') of two observed random variables. The method assumes that the two effects of the confounder are (possibly nonlinear) functions of the confounder plus independent, additive noise. We discuss under which conditions the model is identi…
Paper tackles backwards-compatible data adaptation for confounded covariate and label shifts.
problem Adapt covariates to predict labels confounded with covariate shifts.
method Proposes confounded shift framework based on minimizing divergence between source and target conditional distributions, conditioning on confounders.
result Demonstrates approach on synthetic and real datasets, achieving backwards-compatible data adaptation.