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Found 6 papers, 1 papers with code
Differentiable Causal Discovery Under Unmeasured Confounding
In this work, we derive differentiable algebraic constraints that fully characterize the space of ancestral ADMGs, as well as more general classes of ADMGs, arid ADMGs and bow-free ADMGs, that capture all equality restrictions on the observed variables.
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
However, existing approaches have two important shortcomings: (i) the "explanatory units" are micro-level inputs into the relevant prediction model, e. g., image pixels, rather than interpretable macro-level features that are more useful for understanding how to possibly change the algorithm's behavior, and (ii) existing approaches assume there exists no unmeasured confounding between features and target model predictions, which fails to hold when the explanatory units are macro-level variables.
Optimal Training of Fair Predictive Models
Specifically, we show how to reparameterize the observed data likelihood such that fairness constraints correspond directly to parameters that appear in the likelihood, transforming a complex constrained optimization objective into a simple optimization problem with box constraints.
Causal Inference Under Interference And Network Uncertainty
Classical causal and statistical inference methods typically assume the observed data consists of independent realizations.
Learning Optimal Fair Policies
Systematic discriminatory biases present in our society influence the way data is collected and stored, the way variables are defined, and the way scientific findings are put into practice as policy.
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods.
