EmmaHLU's Stars
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
py-why/dowhy
DoWhy 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.
jvpoulos/causal-ml
Must-read papers and resources related to causal inference and machine (deep) learning
delta-io/delta
An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
apache/spark
Apache Spark - A unified analytics engine for large-scale data processing
C4IROcean/odp-sdk-python
Python SDK for Ocean Data Platform
Cyfrin/foundry-full-course-cu
smartcontractkit/full-blockchain-solidity-course-py
Ultimate Solidity, Blockchain, and Smart Contract - Beginner to Expert Full Course | Python Edition
HCPLab-SYSU/CausalVLR
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning (视觉-语言因果推理开源框架)
py-why/pywhy-llm
Experimental library integrating LLM capabilities to support causal analyses
DeepCANFR/DeepCA---Hybrid-Deep-Learning-Cellular-Automata-Reservoir
BradyFU/Awesome-Multimodal-Large-Language-Models
:sparkles::sparkles:Latest Advances on Multimodal Large Language Models
baddoo/LANDO
Codes for Linear and Nonlinear Disambiguation Optimization (LANDO)
learning-2-learn/nsdmd
Non Stationary Dynamical Mode Decomposition
PyDMD/PyDMD
Python Dynamic Mode Decomposition
BethanyL/DeepKoopman
neural networks to learn Koopman eigenfunctions
PacktPublishing/Causal-Inference-and-Discovery-in-Python
Causal Inference and Discovery in Python by Packt Publishing
basiralab/GNNs-in-Network-Neuroscience
A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.
GaloisInc/dlkoopman
A general-purpose Python package for Koopman theory using deep learning.
yzz673/Brant-2
thuml/Nonstationary_Transformers
Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415
thuml/iTransformer
Official implementation for "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting" (ICLR 2024 Spotlight), https://openreview.net/forum?id=JePfAI8fah
thuml/Time-Series-Library
A Library for Advanced Deep Time Series Models.
thuml/Koopa
Code release for "Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors" (NeurIPS 2023), https://arxiv.org/abs/2305.18803
helange23/from_fourier_to_koopman
Linear and non-linear spectral forecasting algorithms
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
hoechenberger/pybrain_mne
Material for Pybrain 2020 MNE-Python workshop
meagmohit/EEG-Datasets
A list of all public EEG-datasets
psychopy/psychopy
For running psychology and neuroscience experiments