genome_integration
causalml
| genome_integration | causalml | |
|---|---|---|
| 1 | 10 | |
| 11 | 4,770 | |
| - | 1.3% | |
| 0.0 | 8.5 | |
| almost 2 years ago | 10 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
genome_integration
-
[D] Clustering high dimensional
Causal Genomic Analysis
causalml
- uber/causalml: Uplift modeling and causal inference with machine learning algorithms
-
Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
-
Completed 3 months in Microsoft as Data Scientist.
Do you (or Microsoft DS teams in general) tackle any causal problems? Apparently, uber tries to solve these issues, they created https://github.com/uber/causalml
- [R] apd-crs: Cure Rate Survival Analysis in Python
-
[S] Python packages to replace R
There's some causality focused packages. Nothing like scikit-learn or statsmodels yet because it's all based on very recent research, but this one: https://github.com/uber/causalml
-
Good way to segment a/b test results for insight or narrative?
I agree that uplift trees and CATE methods are promising here. If you’re in Python, check out Uber’s open-source causalml.
-
UpliftML: An uplift modeling library that handles web scale datasets
Many libraries have recently emerged that offer implementations of algorithms for heterogeneous treatment effect estimation (or, CATE estimation). The most well-known examples are Microsoft's EconML (https://github.com/microsoft/EconML) and Uber's CausalML (https://github.com/uber/causalml). Existing libraries require all data to fit in memory, which is often a limitation for industry applications on web scale datasets. Booking.com's new library offers similar functionality on top of Spark, enabling web scale uplift modeling.
-
R, I love you.
you like causal inference? it must be nice to be able to use libraires like dowhy, causal ml, and ananke right? 🤔🤔🤔
-
Causal data science
video's author recommends this course in a comment: https://www.coursera.org/learn/crash-course-in-causality and he also co-created this library: https://github.com/uber/causalml
-
Model Re-Training with Intervention Effects
There aren't many general solutions because it will really depend on your domain. There are some packages that specifically work for "modeling under interventions" (like this https://github.com/uber/causalml although I have never tried it). In general, if you have enough data between intervention and not-intervention, you could train two different models and then apply whichever one makes sense (e.g. if you wanted to find the highest-churn-risk users among those who have already had the intervention, use the model trained on the prior intervention cases).
What are some alternatives?
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
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.
CausalPy - A Python package for causal inference in quasi-experimental settings
upliftml - UpliftML: A Python Package for Scalable Uplift Modeling
enformer-pytorch - Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch
awesome-causality-algorithms - An index of algorithms for learning causality with data
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
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.
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
BTYD - BTYD 2.4.3