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Found 44 papers, 25 papers with code
Clustered random forests with correlated data for optimal estimation and inference under potential covariate shift
Clustered random forests are shown for certain tree splitting criteria to be minimax rate optimal for pointwise conditional mean estimation, while being computationally competitive with standard random forests.
Causal Invariance Learning via Efficient Optimization of a Nonconvex Objective
To address both challenges, we focus on the additive intervention regime and propose nearly necessary and sufficient conditions for ensuring that the invariant prediction model matches the causal outcome model.
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning
The challenge of finding such an unknown set of quasi-causal or invariant variables is compounded by the presence of endogenous variables that have heterogeneous effects across different environments.
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields.
Extrapolation-Aware Nonparametric Statistical Inference
In this work, we extend the nonparametric statistical model to explicitly allow for extrapolation and introduce a class of extrapolation assumptions that can be combined with existing inference techniques to draw extrapolation-aware conclusions.
Assessing the overall and partial causal well-specification of nonlinear additive noise models
We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models.
Invariant Probabilistic Prediction
In recent years, there has been a growing interest in statistical methods that exhibit robust performance under distribution changes between training and test data.
Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
To address such potential distribution shifts, we develop an unsupervised domain adaptation approach that leverages labeled data from multiple source domains and unlabeled data from the target domain.
Federated Learning
Multi-Source Unsupervised Domain Adaptation
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Causality-oriented robustness: exploiting general noise interventions
In a linear setting, we prove that DRIG yields predictions that are robust among a data-dependent class of distribution shifts.
TSCI: two stage curvature identification for causal inference with invalid instruments
TSCI implements a two-stage algorithm.
Natural Language-Based Synthetic Data Generation for Cluster Analysis
Cluster analysis relies on effective benchmarks for evaluating and comparing different algorithms.
Confidence and Uncertainty Assessment for Distributional Random Forests
The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions.
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
We leverage this procedure and evaluate the performance of GnIES on an array of synthetic and semi-synthetic data sets, and real data from a biological system and a tightly controlled physical system.
On the Identifiability and Estimation of Causal Location-Scale Noise Models
We study the class of location-scale or heteroscedastic noise models (LSNMs), in which the effect $Y$ can be written as a function of the cause $X$ and a noise source $N$ independent of $X$, which may be scaled by a positive function $g$ over the cause, i. e., $Y = f(X) + g(X)N$.
Treatment Effect Estimation with Observational Network Data using Machine Learning
We develop augmented inverse probability weighting (AIPW) for estimation and inference of the expected average treatment effect (EATE) with observational data from a single (social) network with spillover effects.
Random Forests for Change Point Detection
However, the method can be paired with any classifier that yields class probability predictions, which we illustrate by also using a k-nearest neighbor classifier.
Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
The success of TSCI requires the instrumental variable's effect on treatment to differ from its violation form.
A Fast Non-parametric Approach for Local Causal Structure Learning
DAG-FOCI outputs the set of parents of a response variable of interest.
Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements
Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) in a partially linear mixed-effects model for repeated measurements.
Structure Learning for Directed Trees
Furthermore, we study the identifiability gap, which quantifies how much better the true causal model fits the observational distribution, and prove that it is lower bounded by local properties of the causal model.
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Here, we developed and validated a machine learning (ML) system for the prediction of sepsis in the ICU.
Regularizing Double Machine Learning in Partially Linear Endogenous Models
The linear coefficient in a partially linear model with confounding variables can be estimated using double machine learning (DML).
Methodology Statistics Theory Statistics Theory
Distributional Anchor Regression
In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors.
Methodology
Graphical Elastic Net and Target Matrices: Fast Algorithms and Software for Sparse Precision Matrix Estimation
We consider estimation of undirected Gaussian graphical models and inverse covariances in high-dimensional scenarios by penalizing the corresponding precision matrix.
Methodology Computation
Domain adaptation under structural causal models
Domain adaptation (DA) arises as an important problem in statistical machine learning when the source data used to train a model is different from the target data used to test the model.
Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries
Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data.
Deconfounding and Causal Regularization for Stability and External Validity
We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint.
Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)
In this discussion, we compare the choice of seeded intervals and that of random intervals for change point segmentation from practical, statistical and computational perspectives.
Methodology Computation
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression
Random Forest (Breiman, 2001) is a successful and widely used regression and classification algorithm.
Doubly Debiased Lasso: High-Dimensional Inference under Hidden Confounding
Inferring causal relationships or related associations from observational data can be invalidated by the existence of hidden confounding.
Methodology Statistics Theory Statistics Theory
Stabilizing Variable Selection and Regression
In particular, it is useful to distinguish between stable and unstable predictors, i. e., predictors which have a fixed or a changing functional dependence on the response, respectively.
Methodology Applications
Goodness-of-fit testing in high-dimensional generalized linear models
We propose a family of tests to assess the goodness-of-fit of a high-dimensional generalized linear model.
Methodology Statistics Theory Statistics Theory
Change point detection for graphical models in the presence of missing values
We propose estimation methods for change points in high-dimensional covariance structures with an emphasis on challenging scenarios with missing values.
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise
We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.
Anchor regression: heterogeneous data meets causality
If anchor regression and least squares provide the same answer (anchor stability), we establish that OLS parameters are invariant under certain distributional changes.
Methodology
Kernel-based Tests for Joint Independence
Based on an empirical estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma approximation.
Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagrams
We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs).
Causal inference using invariant prediction: identification and confidence intervals
In contrast, predictions from a non-causal model can potentially be very wrong if we actively intervene on variables.
Methodology
Score-based Causal Learning in Additive Noise Models
Given data sampled from a number of variables, one is often interested in the underlying causal relationships in the form of a directed acyclic graph.
High-dimensional learning of linear causal networks via inverse covariance estimation
We establish a new framework for statistical estimation of directed acyclic graphs (DAGs) when data are generated from a linear, possibly non-Gaussian structural equation model.
CAM: Causal additive models, high-dimensional order search and penalized regression
We develop estimation for potentially high-dimensional additive structural equation models.
Structural Intervention Distance (SID) for Evaluating Causal Graphs
To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID).
Identifiability of Gaussian structural equation models with equal error variances
In this work, we prove full identifiability if all noise variables have the same variances: the directed acyclic graph can be recovered from the joint Gaussian distribution.
MissForest - nonparametric missing value imputation for mixed-type data
Modern data acquisition based on high-throughput technology is often facing the problem of missing data.
