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Found 6 papers, 1 papers with code
Deep Learning With DAGs
As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships.
$ρ$-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.
Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flows
Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE.
Contradistinguisher: A Vapnik's Imperative to Unsupervised Domain Adaptation
To address this, we follow Vapnik's imperative of statistical learning that states any desired problem should be solved in the most direct way rather than solving a more general intermediate task and propose a direct approach to domain adaptation that does not require domain alignment.
CUDA: Contradistinguisher for Unsupervised Domain Adaptation
In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain.
