causalml
causalnex
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| causalml | causalnex | |
|---|---|---|
| 10 | 2 | |
| 4,724 | 2,135 | |
| 2.3% | 1.6% | |
| 8.4 | 6.6 | |
| 6 days ago | 2 months ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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causalml
- uber/causalml: Uplift modeling and causal inference with machine learning algorithms
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Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
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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
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[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
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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.
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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.
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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? 🤔🤔🤔
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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
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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).
causalnex
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How many of you still buy and read textbooks after your degree?
I don't claim to defend that this is actually the right way of dealing with those things, but QuantumBlack gave a talk at neurips a couple years back, and really hyped up their package (https://github.com/quantumblacklabs/causalnex) for dealing with this stuff.
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What are some tools/best practices that Causal Inferencing teams use for experimentation?
As for causal libraries I'd recommend CausalNex, it's the only library that I know that does Judea Pearl's do() operator, and I think that's really great if you want to intervene over causal knowledge (that you'll want).
What are some alternatives?
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.
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.
upliftml - UpliftML: A Python Package for Scalable Uplift Modeling
pgmpy - Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
scikit-learn - scikit-learn: machine learning in Python
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.
causaldag - Python package for the creation, manipulation, and learning of Causal DAGs
BTYD - BTYD 2.4.3
looper - A resource list for causality in statistics, data science and physics
CausalPy - A Python package for causal inference in quasi-experimental settings
Keras - Deep Learning for humans