Search Results for author:
Found 34 papers, 10 papers with code
Evaluating and Correcting Performative Effects of Decision Support Systems via Causal Domain Shift
In this work, we propose to model the deployment of a DSS as causal domain shift and provide novel cross-domain identification results for the conditional expectation $E[Y | X]$, allowing for pre- and post-hoc assessment of the deployment of the DSS, and for retraining of a model that assesses the risk under a baseline policy where the DSS is not deployed.
Modeling Latent Selection with Structural Causal Models
Selection bias is ubiquitous in real-world data, and can lead to misleading results if not dealt with properly.
Establishing Markov Equivalence in Cyclic Directed Graphs
We present a new, efficient procedure to establish Markov equivalence between directed graphs that may or may not contain cycles under the \textit{d}-separation criterion.
Correcting for Selection Bias and Missing Response in Regression using Privileged Information
We conclude that repeated regression can appropriately correct for bias, and can have considerable advantage over weighted regression, especially when extrapolating to regions of the feature space where response is never observed.
Local Constraint-Based Causal Discovery under Selection Bias
We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present.
Combining Interventional and Observational Data Using Causal Reductions
Second, for continuous variables and assuming a linear-Gaussian model, we derive equality constraints for the parameters of the observational and interventional distributions.
Causality and independence in perfectly adapted dynamical systems
Perfect adaptation in a dynamical system is the phenomenon that one or more variables have an initial transient response to a persistent change in an external stimulus but revert to their original value as the system converges to equilibrium.
Robustness of Model Predictions under Extension
Mathematical models of the real world are simplified representations of complex systems.
A Weaker Faithfulness Assumption based on Triple Interactions
One of the core assumptions in causal discovery is the faithfulness assumption, i. e., assuming that independencies found in the data are due to separations in the true causal graph.
Causal Bandits without prior knowledge using separating sets
Methods proposed for this problem thus far in the literature rely on exact prior knowledge of the full causal graph.
A Bayesian Nonparametric Conditional Two-sample Test with an Application to Local Causal Discovery
In these settings, conditional independence testing with $X$ or $Y$ binary (and the other continuous) is paramount to the performance of the causal discovery algorithm.
Statistics Theory Statistics Theory
Conditional independences and causal relations implied by sets of equations
Real-world complex systems are often modelled by sets of equations with endogenous and exogenous variables.
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset.
Boosting Local Causal Discovery in High-Dimensional Expression Data
We study the performance of Local Causal Discovery (LCD), a simple and efficient constraint-based method for causal discovery, in predicting causal effects in large-scale gene expression data.
Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias
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.
An Upper Bound for Random Measurement Error in Causal Discovery
Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error.
Algebraic Equivalence of Linear Structural Equation Models
Despite their popularity, many questions about the algebraic constraints imposed by linear structural equation models remain open problems.
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders
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.
Beyond Structural Causal Models: Causal Constraints Models
Structural Causal Models (SCMs) provide a popular causal modeling framework.
Causal Modeling of Dynamical Systems
We introduce the formal framework of structural dynamical causal models (SDCMs) that explicates the causal semantics of the system's components as part of the model.
Markov Properties for Graphical Models with Cycles and Latent Variables
We investigate probabilistic graphical models that allow for both cycles and latent variables.
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ.
Causal Consistency of Structural Equation Models
Complex systems can be modelled at various levels of detail.
Joint Causal Inference from Multiple Contexts
We explain how several well-known causal discovery algorithms can be seen as addressing special cases of the JCI framework, and we also propose novel implementations that extend existing causal discovery methods for purely observational data to the JCI setting.
Foundations of Structural Causal Models with Cycles and Latent Variables
In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles.
From Deterministic ODEs to Dynamic Structural Causal Models
Structural Causal Models are widely used in causal modelling, but how they relate to other modelling tools is poorly understood.
Ancestral Causal Inference
Constraint-based causal discovery from limited data is a notoriously difficult challenge due to the many borderline independence test decisions.
Distinguishing cause from effect using observational data: methods and benchmarks
We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data.
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.
From Ordinary Differential Equations to Structural Causal Models: the deterministic case
We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM).
On Causal Discovery with Cyclic Additive Noise Models
We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise.
Probabilistic latent variable models for distinguishing between cause and effect
To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive).
Bounds on marginal probability distributions
We propose a novel bound on single-variable marginal probability distributions in factor graphs with discrete variables.
Nonlinear causal discovery with additive noise models
The discovery of causal relationships between a set of observed variables is a fundamental problem in science.
