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Deep Learning With DAGs
As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships.
On the Probability of Immunity
This work is devoted to the study of the probability of immunity, i. e. the effect occurs whether exposed or not.
Bounding the Probabilities of Benefit and Harm Through Sensitivity Parameters and Proxies
We present two methods for bounding the probabilities of benefit and harm under unmeasured confounding.
$ρ$-GNF : A Novel Sensitivity Analysis Approach Under Unobserved Confounders
We propose a new sensitivity analysis model that combines copulas and normalizing flows for causal inference under unobserved confounding.
Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows
Thus, our article shows how c-GNFs further the use of deep learning and causal inference in AI for social good.
Simple yet Sharp Sensitivity Analysis for Unmeasured Confounding
We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding.
On the Non-Monotonicity of a Non-Differentially Mismeasured Binary Confounder
Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder.
On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder
Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder.
Factorization of the Partial Covariance in Singly-Connected Path Diagrams
We extend path analysis by showing that, for a singly-connected path diagram, the partial covariance of two random variables factorizes over the nodes and edges in the path between the variables.
Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
An intervention may have an effect on units other than those to which it was administered.
Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances
Specifically, we prove identifiability for the Gaussian structural equation models that can be represented as Andersson-Madigan-Perlman chain graphs (Andersson et al., 2001).
Identification of Strong Edges in AMP Chain Graphs
However, the directed edges in the essential graph are not necessarily strong or invariant, i. e. they may not be shared by every member of the equivalence class.
Unifying DAGs and UGs
We introduce a new class of graphical models that generalizes Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed acyclity constraint so that only directed cycles are forbidden.
Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models
We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles.
Representing Independence Models with Elementary Triplets
In an independence model, the triplets that represent conditional independences between singletons are called elementary.
Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs
We extend Andersson-Madigan-Perlman chain graphs by (i) relaxing the semidirected acyclity constraint so that only directed cycles are forbidden, and (ii) allowing up to two edges between any pair of nodes.
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs
We address some computational issues that may hinder the use of AMP chain graphs in practice.
Every LWF and AMP chain graph originates from a set of causal models
This paper aims at justifying LWF and AMP chain graphs by showing that they do not represent arbitrary independence models.
Error AMP Chain Graphs
We will also show that every EAMP CG under marginalization of the error nodes is Markov equivalent to some LWF CG under marginalization of the error nodes, and that the latter is Markov equivalent to some directed and acyclic graph (DAG) under marginalization of the error nodes and conditioning on some selection nodes.
Marginal AMP Chain Graphs
For Gaussian probability distributions, we also show that every MAMP chain graph is Markov equivalent to some directed and acyclic graph with deterministic nodes under marginalization and conditioning on some of its nodes.
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness: Extended Version
In particular, we present a constraint based algorithm for learning an AMP chain graph a given probability distribution is faithful to.
Approximate Counting of Graphical Models Via MCMC Revisited
In Pe\~na (2007), MCMC sampling is applied to approximately calculate the ratio of essential graphs (EGs) to directed acyclic graphs (DAGs) for up to 20 nodes.
