db-benchmark VS causalml

Compare db-benchmark vs causalml and see what are their differences.

db-benchmark

reproducible benchmark of database-like ops (by h2oai)

causalml

Uplift modeling and causal inference with machine learning algorithms (by uber)
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db-benchmark causalml
32 4
188 2,466
0.5% 3.5%
8.5 7.9
2 months ago 5 days ago
R Python
Mozilla Public License 2.0 GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

db-benchmark

Posts with mentions or reviews of db-benchmark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-24.

causalml

Posts with mentions or reviews of causalml. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-26.
  • UpliftML: An uplift modeling library that handles web scale datasets
    3 projects | news.ycombinator.com | 26 Sep 2021
    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.
    3 projects | reddit.com/r/datascience | 25 Jun 2021
    you like causal inference? it must be nice to be able to use libraires like dowhy, causal ml, and ananke right? 🤔🤔🤔
  • Causal data science
    1 project | reddit.com/r/datascience | 11 Mar 2021
    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
    1 project | reddit.com/r/datascience | 20 Feb 2021
    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?

When comparing db-benchmark and causalml you can also consider the following projects:

arrow-datafusion - Apache Arrow DataFusion and Ballista query engines

polars - Fast multi-threaded DataFrame library in Rust and Python

causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.

upliftml - UpliftML: A Python Package for Scalable Uplift Modeling

DataFramesMeta.jl - Metaprogramming tools for DataFrames

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.

csvs-to-sqlite - Convert CSV files into a SQLite database

sktime - A unified framework for machine learning with time series

Preql - An interpreted relational query language that compiles to SQL.

pyodide - Python with the scientific stack, compiled to WebAssembly.

PackageCompiler.jl - Compile your Julia Package

tidytable - Tidy interface to 'data.table'