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py-why / DowhyDoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
uber / CausalmlUplift modeling and causal inference with machine learning algorithms
facebookresearch / DenoiserReal Time Speech Enhancement in the Waveform Domain (Interspeech 2020)We provide a PyTorch implementation of the paper Real Time Speech Enhancement in the Waveform Domain. In which, we present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities.
Robbyant / Lingbot VaCausal video-action world model for generalist robot control
BiomedSciAI / CausallibA Python package for modular causal inference analysis and model evaluations
altdeep / CausalAIThe open source repository for the Causal Modeling in Machine Learning Workshop at Altdeep.ai @ www.altdeep.ai/courses/causalAI
Tencent / WeDLMWeDLM: The fastest diffusion language model with standard causal attention and native KV cache compatibility, delivering real speedups over vLLM-optimized baselines.
AMLab-Amsterdam / CEVAECausal Effect Inference with Deep Latent-Variable Models
kochbj / Deep Learning For Causal InferenceExtensive tutorials for learning how to build deep learning models for causal inference (HTE) using selection on observables in Tensorflow 2 and Pytorch.
jtextor / DagittyGraphical analysis of structural causal models / graphical causal models.
NIRVANALAN / STream3RDynamic 3D Foundation Model using Causal Transformer. [ICLR 2026]
biomedia-mira / DeepscmRepository for Deep Structural Causal Models for Tractable Counterfactual Inference
zju3dv / MotionStreamer[ICCV 2025] MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space
ijmbarr / CausalgraphicalmodelsCausal Graphical Models in Python
tcassou / Causal ImpactPython package for causal inference using Bayesian structural time-series models.
mschauer / CausalInference.jlCausal inference, graphical models and structure learning in Julia
gyorilab / IndraINDRA (Integrated Network and Dynamical Reasoning Assembler) is an automated model assembly system interfacing with NLP systems and databases to collect knowledge, and through a process of assembly, produce causal graphs and dynamical models.
guidelabs / SteerlingInterpretable Causal Diffusion Language Models
Tencent / Fast Causal InferenceIt is a high-performance causal inference (statistical model) computing library based on OLAP, which solves the performance bottleneck of the existing statistical model library (R/Python) under big data
OpenMOSS / MOSS Audio TokenizerMOSS-Audio-Tokenizer is a Causal Transformer-based audio tokenizer built on the CAT architecture. Trained on 3M hours of diverse audio, it supports streaming and variable bitrates, delivering SOTA reconstruction and strong performance in generation and understanding—serving as a unified interface for next-generation native audio language models.
