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Found 9 papers, 6 papers with code
Functional Causal Bayesian Optimization
We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph.
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach
We propose a kernel-based nonparametric estimator for the causal effect when the cause is corrupted by error.
Beyond Impossibility: Balancing Sufficiency, Separation and Accuracy
Among the various aspects of algorithmic fairness studied in recent years, the tension between satisfying both \textit{sufficiency} and \textit{separation} -- e. g. the ratios of positive or negative predictive values, and false positive or false negative rates across groups -- has received much attention.
Operationalizing Complex Causes: A Pragmatic View of Mediation
We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Necessity and sufficiency are the building blocks of all successful explanations.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Differentiable Causal Backdoor Discovery
Discovering the causal effect of a decision is critical to nearly all forms of decision-making.
Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops
While humor is often thought to be beyond the reach of Natural Language Processing, we show that several aspects of single-word humor correlate with simple linear directions in Word Embeddings.
