GenPark AI Agent Skill - Structural Causal Model (SCM) DAG engine evaluating observational distributions and simulating Pearl's do-calculus interventions.
GenPark AI Agent Skill - Propensity score matching and Inverse Probability Weighting (IPW) estimator calculating Average Treatment Effect (ATE) under conditional ignorability.
GenPark AI Agent Skill - Graph-theoretic backdoor criterion validator and minimal confounder adjustment set identifier ensuring unconfounded causal effect estimation.
GenPark AI Agent Skill - Structural Causal Model (SCM) DAG engine evaluating observational distributions and simulating Pearl's do-calculus interventions.
GenPark AI Agent Skill - Pearl's 3-step counterfactual inference engine executing Abduction, Action (do-surgery), and Prediction to answer what-if causal inquiries.
GenPark AI Agent Skill - Instrumental Variable (IV) Two-Stage Least Squares (2SLS) causal estimator isolating unobserved confounding and testing instrument strength.
GenPark AI Agent Skill - Instrumental Variable (IV) Two-Stage Least Squares (2SLS) causal estimator isolating unobserved confounding and testing instrument strength.
GenPark AI Agent Skill - Propensity score matching and Inverse Probability Weighting (IPW) estimator calculating Average Treatment Effect (ATE) under conditional ignorability.
GenPark AI Agent Skill - Graph-theoretic backdoor criterion validator and minimal confounder adjustment set identifier ensuring unconfounded causal effect estimation.
GenPark AI Agent Skill - Pearl's 3-step counterfactual inference engine executing Abduction, Action (do-surgery), and Prediction to answer what-if causal inquiries.
Demystifying Judea Pearl's do-calculus and the Backdoor Criterion by hand. Resolving a curated Simpson's Paradox using structural causal modeling on superhero battle mechanics.
Models causals estructurals i resultats potencials (Treball de Final de Grau)
Paper and Lean 4 mechanization for Graph Surgery and the Do-Operator
Dokumentasi lengkap Tugas 3 Causal Inference - 6 latihan menggunakan R & RStudio mencakup DAG, Simpson's Paradox, Backdoor Adjustment, Counterfactual, CausalImpact, dan Mediation Analysis
Stress-testing causal discovery under hidden confounding using causalXtreme and extremeSCM, with reproducible R experiments on spurious causal edges and false-positive control.
Exploring causal inference from first principles — Pearl's DAGs & do-calculus vs Rubin's potential outcomes framework · DoWhy · Python
Efficient and diverse synthetic causal priors in JAX for end-to-end pretraining
Causal intent monitoring for LangGraph agents.
A first-class calculus for mechanism-level causal intervention. Strictly extends Pearl's SCM framework with hypergraph mechanisms as primary causal objects.
Applied Causal Inference framework: DAGs, d-separation, Structural Causal Models, and Meta-Learners (S/T/X) to isolate true policy treatment effects beyond passive correlation.
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