Local nonlinear causal attention latent diffusion models for visual story synthesizing

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).

Nonlinear Causal Discovery with Confounders

Code used in the paper "Score matching enables causal discovery of nonlinear additive noise models", Rolland et al., ICML 2022

Boosting Synthetic Data Generation with Effective Nonlinear Causal Discovery

Code for a variety of nonlinear conditional independence tests and 'nonlinear Invariant Causal Prediction' to estimate the causal parents of a given target variable from data collected in different experimental or environmental conditions, extending 'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016) to nonlinear settings.

This code provide the CANM algorithim for causal discovery. Please cite "Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Cascade Nonlinear Additive Noise Models. IJCAI 2019."

nl-causal: nonlinear causal inference based on IV regression in Python

Code for Nature paper, causality of nodal time-series observations.

A multivariate causal discovery based on post-nonlinear model

Causal weights for macroeconomic shocks

Ordering-Based Causal Discovery for Linear and Nonlinear Relations. (Accepted by NeurIPS 2024)

Nonlinear Granger causality test based on random forest (source code of "A Causal Inference Model based on Random Forest to identify soil moisture-precipitation feedback", Journal of Hydrometeorology)

A Python 3 package for identifying distribution shifts (a.k.a feature-shifts) between datasets. Official implementation of the paper: "iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models".

TechBlog: 初心者のための時系列間の因果関係検出

Code for the paper "Enabling Causal Discovery in Post-Nonlinear Models with Normalizing Flows" - ECAI 2024

Here we provide scripts used in 'Investigating causal relations between sleep duration and risks of adverse pregnancy and perinatal outcomes: Linear and nonlinear Mendelian randomization analyses in up to 356,069 European women'

Collection of Python code written during my time at Stephan Munch's Lab. Included is my implementation of a Gaussian Process called KenGP, which is a nonlinear, (mostly) nonparametric prediction tool. I also have implementations for Empirical Dynamical Modelling(like GP but much simpler), Convergent Cross Mapping(detects causal relationships between time series), Diffusion Maps(nonlinear dimensionality reduction), Slow Feature Analysis(extraction of slowly changing of dataset), and False Nearest Neighbors(for finding an adequate time delay embedding dimension for a dataset), all of which are methods introduced used in and around the nonlinear dynamics literature.

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Ranked-Based Causal Discovery for Post-Nonlinear Models

This code provide the CANM algorithim for causal discovery. Please cite "Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao. Causal Discovery with Cascade Nonlinear Additive Noise Models. IJCAI 2019."

Implementing the paper "Nonlinear causal discovery with additive noise models" by PO Hoyer

Calculations for "Nonlinear causality of Israel-Stewart theory with diffusion". Preprint: https://arxiv.org/abs/2507.20064

R package for analysis templates: ECM with MARS nonlinearity, Bayesian Structural Time Series, and Bayesian GLM with AR(1). Designed to determine causal direction between pairs of economic variables, also useful for other scientific studies of phenomenon with equivalent underlying logic. Packed with pipeline tools and method guides.

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Exploring Non-linear Causal Inference using GAM and HSIC

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any software codes for this

causal discovery algorithm geared towards continuous, non-linear data