On the unity between observational and experimental causal discovery

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

Discovering and understanding the causal relationships underlying natural phenomena is important for many scientific disciplines, such as economics, computer science, education, medicine and biology. Meanwhile, new knowledge is revealed by discovering causal relationships from data. The causal discovery approach can be characterized as causal structure learning, where variables and their conditional dependencies are represented by a directed acyclic graph. Hence, causal structure discovery methods are necessary for discovering causal relationships from data. In this survey, we review the background knowledge and the causal discovery methods comprehensively. These methods are isolated into four categories, including constraint-based methods, score-based methods, functional causal models based methods and continuous optimization based methods. We mainly focus on the advanced methods which leverage continuous optimization. In addition, we introduce commonly utilized benchmark datasets and open source codes for researchers to evaluate and apply causal discovery methods.KeywordsCausal discoveryCausal structure learningDirected acyclic graphsContinuous optimization

Recovering the structure of causal graphical models from observational data is an essential yet challenging task for causal discovery in scientific scenarios. Domain-specific causal discovery usually relies on expert validation or prior analysis to improve the reliability of recovered causality, which is yet limited by the scarcity of expert resources. Recently, Large Language Models (LLM) have been used for causal analysis across various domain-specific scenarios, suggesting its potential as autonomous expert roles in guiding data-based structure learning. However, integrating LLMs into causal discovery faces challenges due to inaccuracies in LLM-based reasoning on revealing the actual causal structure. To address this challenge, we propose an error-tolerant LLM-driven causal discovery framework. The error-tolerant mechanism is designed three-fold with sufficient consideration on potential inaccuracies. In the LLM-based reasoning process, an accuracy-oriented prompting strategy restricts causal analysis to a reliable range. Next, a knowledge-to-structure transition aligns LLM-derived causal statements with structural causal interactions. In the structure learning process, the goodness-of-fit to data and adherence to LLM-derived priors are balanced to further address prior inaccuracies. Evaluation of eight real-world causal structures demonstrates the efficacy of our LLM-driven approach in improving data-based causal discovery, along with its robustness to inaccurate LLM-derived priors. Codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tyMadara/LLM-CD</uri>.

Causal discovery from sequential data in ALS disease based on entropy criteria.

Causal discovery by continuous optimization with weighted superstructure.

A fundamental task in various disciplines of science, including biology, is to find underlying causal relations and make use of them. Causal relations can be seen if interventions are properly applied; however, in many cases they are difficult or even impossible to conduct. It is then necessary to discover causal relations by analyzing statistical properties of purely observational data, which is known as causal discovery or causal structure search. This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.

The common interpretation of Bayesian networks is that they are vehicles for representing probability distributions, in a graphical form supportive of human understanding and with computational mechanisms supportive of probabilistic reasoning (updating). But the interpretation of Bayesian networks assumed by causal discovery algorithms is causal: the links in the graphs specifically represent direct causal connections between variables. However, there is some tension between these two interpretations. The philosophy of probabilistic causation posits a particular connection between the two, namely that causal relations of certain kinds give rise to probabilistic relations of certain kinds. Causal discovery algorithms take advantage of this kind of connection by ruling out some Bayesian networks given observational data not supported by the posited probability-causality relation. But the discovered (remaining) Bayesian networks are then specifically causal, and not simply arbitrary representations of probability.There are multiple contentious issues underlying any causal interpretation of Bayesian networks. We will address the following questions: Since Bayesian net construction rules allow the construction of multiple distinct networks to represent the very same probability distribution, how can we come to prefer any specific one as “the” causal network? Since Bayesian nets within a Verma-Pearl pattern are strongly indistinguishable, how can causal discovery ever come to select exactly one network as “the” causal network? Causal discovery assumes faithfulness (that d-connections in the model are accompanied by probabilistic dependency in the system modeled). However, some physical systems cannot be modeled faithfully under a causal interpretation. How can causal discovery cope with that? Here we introduce a causal interpretation of Bayesian networks by way of answering these questions and then apply this interpretation to answering further questions about causal power, explanation and responsibility.Keywordscausal discoveryfaithfulnessBayesian networksprobabilistic causalityinterventioncausal powercausal responsibility

Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods.

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.

We use a nascent data-driven causal discovery method to find and compare causal relationships in observed data and climate model output. We consider ten different features in the Arctic climate collected from public databases on observational and Energy Exascale Earth System Model (E3SM) data. In identifying and analyzing the resulting causal networks, we make meaningful comparisons between observed and climate model interdependencies. This work demonstrates our ability to apply the PCMCI causal discovery algorithm to Arctic climate data, that there are noticeable similarities between observed and simulated Arctic climate dynamics, and that further work is needed to identify specific areas for improvement to better align models with natural observations.

Understanding causality and the mechanisms underlying psychological phenomena has been a cornerstone of psychological research, with significant implications for theory development and intervention design. While traditional methods such as experimental manipulations or structural equation modelling have been extensively used to explore causal relationships, recent advances in causal inference studies have introduced causal discovery methods as a powerful alternative. These methods can uncover complex causal structures from observational or interventional data, offering unique utility such as identifying causal directions and handling complex causal networks involving numerous variables. This paper provides an introduction to causal discovery methods tailored for psychological research. We review foundational concepts, introduce the classical algorithms, and present a practical tutorial in R, equipping researchers with tools to implement these methods on psychological data. We also discuss the potential of these methods to accelerate theory formation, refine interventions, and foster cross-disciplinary understanding of human cognition as well as the challenges of integrating causal discovery into psychological research.

Causal discovery is essential across various scientific fields to uncover causal structures within data. Traditional methods relying on observational data have limitations due to confounding variables. This paper presents an optimization-based approach using integer programming (IP) to design minimal intervention sets that ensure causal structure identifiability. Our method provides exact and modular solutions, adaptable to different experimental settings and constraints. We demonstrate its effectiveness through comparative analysis across different settings demonstrating its applicability and robustness.

Mining combined causes in large data sets

We review current research in fuzzy cognitive map (FCM) parameter estimation techniques and present results for a method based on constrained optimization as motivation for a discussion of the core issue of causal discovery in intelligent systems. While FCM parameter estimation techniques show utility for automatically determining causal strength from state observations, their success relies heavily on background knowledge about the causal structure of the system, acquired from interviews with domain experts. In this paper we identify causal discovery as a fundamental task for automated FCM synthesis and present a survey of related research in automatic causal discovery that provides a basis for future work in the area of autonomous FCM synthesis

Causal networks provide a mechanistic understanding of clinical phenomena, allowing for personalized and explainable decision-making. Causal discovery, namely the task of constructing such models, is challenging, particularly for rare diseases, where observational data are sparse, medical knowledge is incomplete, and diseases behave over time. This work proposes a new and original expert-in-the-loop causal discovery workflow that iteratively refines a set of causal networks associated with different disease mechanisms.When applied to soft tissue sarcoma, a heterogeneous group of rare cancers, the workflow allows for the first comprehensive causal description of the disease's natural history. Indeed, three causal networks associated with different disease mechanisms shed light on the complex interplay between patients' covariates and disease behavior. These results have the potential to enhance clinical decision-making by allowing the development of personalized treatment strategies.The proposed workflow paves the way to agile, modular, and flexible causal discovery for clinical domains characterized by data sparsity, longitudinal dynamics, and heterogeneous expert knowledge.