jcharit1
Experienced data scientist with a track record of using statistical inference, machine learning, programming, and creativity to drive business value.
@GoogleNew York, New York
jcharit1's Stars
py-why/pywhy-stats
Python package for (conditional) independence testing and statistical functions related to causality.
py-why/causaltune
AutoML for causal inference.
py-why/causal-learn
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
py-why/pywhy-llm
Experimental library integrating LLM capabilities to support causal analyses
py-why/dodiscover
[Experimental] Global causal discovery algorithms
py-why/pywhy-graphs
[Experimental] Causal graphs that are networkx-compliant for the py-why ecosystem.
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.
Yu-Group/simChef
An R package to facilitate PCS simulation studies.
Yu-Group/veridical-flow
Making it easier to build stable, trustworthy data-science pipelines based on the PCS framework.
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.
ACCLAB/DABEST-python
Data Analysis with Bootstrapped ESTimation
hyperopt/hyperopt-sklearn
Hyper-parameter optimization for sklearn
hyperopt/hyperopt
Distributed Asynchronous Hyperparameter Optimization in Python
scikit-optimize/scikit-optimize
Sequential model-based optimization with a `scipy.optimize` interface
tslearn-team/tslearn
The machine learning toolkit for time series analysis in Python
aloctavodia/BAP
Bayesian Analysis with Python (Second Edition)
facebook/prophet
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Tencent/PocketFlow
An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.
facebookresearch/ReAgent
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
dmbee/seglearn
Python module for machine learning time series:
bashtage/linearmodels
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
bashtage/arch
ARCH models in Python
Spandan-Madan/DeepLearningProject
An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch.
jon77lee/JLee_LinearOptimizationBook
QuantEcon/QuantEcon.py
A community based Python library for quantitative economics
reiinakano/scikit-plot
An intuitive library to add plotting functionality to scikit-learn objects.
ethereon/caffe-tensorflow
Caffe models in TensorFlow
ageron/handson-ml
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
pwharrison/modern-nlp-in-python
JonathanReeve/workshop-text-analysis-spacy
Materials for the workshop Advanced Text Analysis with SpaCy and Scikit-Learn, given at NYU during NYCDH Week 2017, at PyData NYC in Nov. 2017, and at Columbia University in 2018 and 2019.