❓y0 (pronounced "why not?") is for causal inference in Python

This is the code for the paper Jacobian-based Causal Discovery with Nonlinear ICA, demonstrating how identifiable representations (particularly, with Nonlinear ICA) can be used to extract the causal graph from an underlying structural equation model (SEM).

Code and real data for "Counterfactual Temporal Point Processes", NeurIPS 2022

Generate dynamic structural causal models from biological knowledge graphs encoded in the Biological Expression Language (BEL)

[MM'2024] Efficient Dual-Confounding Eliminating for Weakly-supervised Temporal Action Localization

Python project on Structural Causal Models and Reinforcement Learning, at Utrecht University.

Structural Causal Model (SCM) approach to explainable reinforcement learning

Temporal Causal-based Simulation (TCS)

End-to-End PyTorch implementation of Chang & Kim's (2026) iVDFM pipeline, Includes: amortized encoder, RegimeNet simplex embeddings, Laplace PriorNet, diagonal AR(p) companion-form dynamics, and injective MLP decoder. Trained via ELBO with reparameterized Laplace innovations. Benchmarked against iTransformer and TimeMixer baselines.

A causal inference-based portfolio optimization framework that models causal relationships between macro factors and asset clusters using DAGs.

Code for presenting and evaluating search algorithms for actual causes identification

🧠 Causal Digital Twin for Marketing at Scale · Predict any marketing decision before you spend a dollar.

ESA-2SCM for Causal Discovery: Causal Modeling with Elastic Segmentation-based Synthetic Instrumental Variable

Counterfactual inference for 3D brain imaging using deep structural causal models.

Code for the python model `actualcauses` that implements algorithms for HP-causes identification.

Code for the paper "Adversarial Causal Tuning for Realistic Time-series Generation".

Causal fairness audit and counterfactual recourse engine using DoWhy DAGs, EconML Double Machine Learning, and structural causal models on the UCI Adult dataset. Amazon ML Summer School 2026 — Causal Inference track.

Fidelity-gated synthetic SCM that stress-tests probability-of-default modeling under selective labels and reports the model's honest operating frontier. The do() oracle real lending data can't be. Grades g-computation vs naive conditioning against planted ground truth. sklearn-only; 66 tests; fully deterministic.