Causal Learning From Predictive Modeling for Observational Data

Ramanan, Nandini; Natarajan, Sriraam

doi:10.3389/fdata.2020.535976

Cited by 13 publications

(8 citation statements)

References 42 publications

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“…We compared the DARLS algorithm to the following DAG structure learning methods: the standard greedy hill climbing (HC) algorithm (Gámez et al 2011), the Peter-Clark (PC) algorithm (Spirtes and Glymour 1991), the max-min hill-climbing (MMHC) algorithm (Tsamardinos et al 2006), the fast greedy equivalence search (FGES) (Chickering 2002;Ramsey et al 2017;Ramanan and Natarajan 2020), and the NOTEARS algorithm (Zheng et al 2018). Among these methods, PC is a constraint-based method and MMHC is a hybrid method.…”

Section: Methodsmentioning

confidence: 99%

Distributed Learning of Generalized Linear Causal Networks

Ye¹,

Amini²,

Zhou³

2022

Preprint

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We consider the task of learning causal structures from data stored on multiple machines, and propose a novel structure learning method called distributed annealing on regularized likelihood score (DARLS) to solve this problem. We model causal structures by a directed acyclic graph that is parameterized with generalized linear models, so that our method is applicable to various types of data. To obtain a high-scoring causal graph, DARLS simulates an annealing process to search over the space of topological sorts, where the optimal graphical structure compatible with a sort is found by a distributed optimization method. This distributed optimization relies on multiple rounds of communication between local and central machines to estimate the optimal structure. We establish its convergence to a global optimizer of the overall score that is computed on all data across local machines. To the best of our knowledge, DARLS is the first distributed method for learning causal graphs with such theoretical guarantees. Through extensive simulation studies, DARLS has shown competing performance against existing methods on distributed data, and achieved comparable structure learning accuracy and test-data likelihood with competing methods applied to pooled data across all local machines. In a real-world application for modeling protein-DNA binding networks with distributed ChIP-Sequencing data, DARLS also exhibits higher predictive power than other methods, demonstrating a great advantage in estimating causal networks from distributed data.

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Section: Methodsmentioning

confidence: 99%

Distributed Learning of Generalized Linear Causal Networks

Ye¹,

Amini²,

Zhou³

2022

Preprint

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“…These are often limited in terms of user-controllable parameters, with structures being sampled uniformly from the space of DAGs, or limited in terms of variation in topology. Other studies use standard benchmark datasets ( Scutari et al, 2019 ; Ramanan and Natarajan, 2020 ). A flexible synthetic generation system would allow the user to specify many parameters which influence the BN generation, in order to match a given real dataset as closely as possible.…”

Section: Methodsmentioning

confidence: 99%

Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks

Butcher

Huang²,

Robinson

et al. 2021

Front. Artif. Intell.

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Developing data-driven solutions that address real-world problems requires understanding of these problems’ causes and how their interaction affects the outcome–often with only observational data. Causal Bayesian Networks (BN) have been proposed as a powerful method for discovering and representing the causal relationships from observational data as a Directed Acyclic Graph (DAG). BNs could be especially useful for research in global health in Lower and Middle Income Countries, where there is an increasing abundance of observational data that could be harnessed for policy making, program evaluation, and intervention design. However, BNs have not been widely adopted by global health professionals, and in real-world applications, confidence in the results of BNs generally remains inadequate. This is partially due to the inability to validate against some ground truth, as the true DAG is not available. This is especially problematic if a learned DAG conflicts with pre-existing domain doctrine. Here we conceptualize and demonstrate an idea of a “Causal Datasheet” that could approximate and document BN performance expectations for a given dataset, aiming to provide confidence and sample size requirements to practitioners. To generate results for such a Causal Datasheet, a tool was developed which can generate synthetic Bayesian networks and their associated synthetic datasets to mimic real-world datasets. The results given by well-known structure learning algorithms and a novel implementation of the OrderMCMC method using the Quotient Normalized Maximum Likelihood score were recorded. These results were used to populate the Causal Datasheet, and recommendations could be made dependent on whether expected performance met user-defined thresholds. We present our experience in the creation of Causal Datasheets to aid analysis decisions at different stages of the research process. First, one was deployed to help determine the appropriate sample size of a planned study of sexual and reproductive health in Madhya Pradesh, India. Second, a datasheet was created to estimate the performance of an existing maternal health survey we conducted in Uttar Pradesh, India. Third, we validated generated performance estimates and investigated current limitations on the well-known ALARM dataset. Our experience demonstrates the utility of the Causal Datasheet, which can help global health practitioners gain more confidence when applying BNs.

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“…Because of its design and consequently causal properties, this dataset can be used to evaluate methods in terms of forecasting and terms of generated patterns. This dataset was adopted in Ramanan and Natarajan (2020) to study how context-specific independencies can be used to learn causal algorithms. A sample from this dataset is shown in Figure 4.…”

Section: Datasetsmentioning

confidence: 99%

Methods and tools for causal discovery and causal inference

Nogueira

Pugnana

Ruggieri

et al. 2022

WIREs Data Min & Knowl

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Causality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation‐based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples. This article is categorized under: Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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Causal Learning From Predictive Modeling for Observational Data

Cited by 13 publications

References 42 publications

Distributed Learning of Generalized Linear Causal Networks

Distributed Learning of Generalized Linear Causal Networks

Causal Datasheet for Datasets: An Evaluation Guide for Real-World Data Analysis and Data Collection Design Using Bayesian Networks

Methods and tools for causal discovery and causal inference

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