In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step. The library is available at https://github.com/microsoft/dowhy
Wise Plc（Wise公司）；OSG Digital（OSG数字公司）；University of Oxford（牛津大学）；University of Tartu（塔尔图大学）
Recently, many causal estimators for Conditional Average Treatment Effect (CATE) and instrumental variable (IV) problems have been published and open sourced, allowing to estimate granular impact of both randomized treatments (such as A/B tests) and of user choices on the outcomes of interest. However, the practical application of such models has ben hampered by the lack of a valid way to score the performance of such models out of sample, in order to select the best one for a given application. We address that gap by proposing novel scoring approaches for both the CATE case and an important s…
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；Guangdong University of Technology（广东工业大学）；University of Melbourne（墨尔本大学）；Shiga University（滋贺大学）；RIKEN（理化学研究所）
$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery
Technical University of Munich（慕尼黑工业大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；University of Applied Sciences Kempten（肯普滕应用科学大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies
Bayesian Artificial Intelligence research lab, MInDS research group（贝叶斯人工智能研究实验室，MInDS研究小组）；School of Electronic Engineering and Computer Science, Queen Mary University of London（电子工程与计算机科学学院，伦敦女王玛丽大学）
From Causal Discovery to Implementation: An Agentic AI Framework for E-Scooter Mobility Hub Planning Across 29 German Cities
Fraunhofer Institute for Industrial Engineering IAO（弗劳恩霍夫工业工程研究所）；Department of Operations & Technology, Heilbronn Data Science Center, Munich Data Science Institute, Technical University of Munich（慕尼黑工业大学运营与技术系、海尔布隆数据中心、慕尼黑数据科学研究所）
Our evolution as a species made a huge step forward when we understood the relationships between causes and effects. These associations may be trivial for some events, but they are not in complex scenarios. To rigorously prove that some occurrences are caused by others, causal theory and causal inference were formalized, introducing the $do$-operator and its associated rules. The main goal of this report is to review and implement in Python some algorithms to compute conditional and non-conditional causal queries from observational data. To this end, we first present some basic background know…
Estimating Causal Effects from Data Generated by Stochastic Algorithms
Graduate School of Business, Stanford University（斯坦福大学商学院）；Graduate School of Business and Department of Economics, Stanford University（斯坦福大学商学院与经济系）；Institute of Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）
In this article, we introduce the BNPqte R package which implements the Bayesian nonparametric approach of Xu, Daniels and Winterstein (2018) for estimating quantile treatment effects in observational studies. This approach provides flexible modeling of the distributions of potential outcomes, so it is capable of capturing a variety of underlying relationships among the outcomes, treatments and confounders and estimating multiple quantile treatment effects simultaneously. Specifically, this approach uses a Bayesian additive regression trees (BART) model to estimate the propensity score and a D…
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
CNRS, MAP5（法国国家科学研究中心，MAP5）；Université Paris Cité（巴黎西岱大学）；CentraleSupélec（中央理工-高等电力学院）；Université Paris-Saclay（巴黎-萨克雷大学）；University of Copenhagen（哥本哈根大学）
Bielefeld University（比勒费尔德大学）；University of Marburg（马尔堡大学）
Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects. To support the finding of causal features in biomedical experiments, we hereby present FRI, an open source Python library that can be used to identify all-relevant variables in linear classification and (ordinal) regression problems. Using the recently proposed feature relevance method, FRI is able to provide the base for further general experi…
