26 papers · ranked by Valyu relevance
Michael P. Grosz, Julia M. Rohrer, Felix Thoemmes
Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and…
M Höfler
Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal…
Qing Wang, Qiao Wang, Ru-Yuan Zhang
In this review, we limit our discussion of causal inference to the evaluation of causal effects rather than the identification of causal mechanisms. Causal inference can be conducted by (i) formulating the research question in a causal framework; (ii) specifying assumptions based on which causal effects can be…
Ivan Olier, Yiqiang Zhan, Xiaoyu Liang, Victor Volovici
Observational studies using causal inference frameworks can provide a feasible alternative to randomized controlled trials. Advances in statistics, machine learning, and access to big data facilitate unraveling complex causal relationships from observational data across healthcare, social sciences, and other fields.…
Musfiqur Rahman Sazal, Vitalii Stebliankin, Kalai Mathee, Giri Narasimhan
Inferring causal effects is critically important in biomedical research as it allows us to move from the typical paradigm of associational studies to causal inference, and can impact treatments and therapeutics. Association patterns can be coincidental and may lead to wrong inferences in complex systems. Microbiomes…
Jorawar Singh, Kishor Bharti, Arvind Arvind
The advances in Artificial Intelligence (AI) and Machine Learning (ML) have opened up many avenues for scientific research, and are adding new dimensions to the process of knowledge creation. However, even the most powerful and versatile of ML applications till date are primarily in the domain of analysis of…
Neil Pearce, Debbie A Lawlor
Introduction It is perhaps not too great an exaggeration to say that Judea Pearl’s work has had a profound effect on the theory and practice of epidemiology. Pearl’s most striking contribution has been his marriage of the counterfactual and probabilistic approaches to causation.1 The resulting toolkit, particularly the…
Riko Kelter
Background Causal inference has seen an increasing popularity in medical research. Estimation of causal effects from observational data allows to draw conclusions from data when randomized controlled trials cannot be conducted. Although the identification of structural causal models (SCM) and the calculation of…
Kevin Cummiskey, Karsten Lübke
Statisticians and data scientists transform raw data into understanding and insight. Ideally, these insights empower people to act and make better decisions. However, data is often misleading especially when trying to draw conclusions about causality (for example, Simpson’s paradox). Therefore, developing causal…
Finnian Lattimore, Cheng Soon Ong
We provide a conceptual map to navigate causal analysis problems. Focusing on the case of discrete random variables, we consider the case of causal effect estimation from observational data. The presented approaches apply also to continuous variables, but the issue of estimation becomes more complex. We then introduce…
Jingying Zeng, Run Wang
Causal inference is a science with multi-disciplinary evolution and applications. On the one hand, it measures effects of treatments in observational data based on experimental designs and rigorous statistical inference to draw causal statements. One of the most influential framework in quantifying causal effects is…
Jiaxin Wang, Yilong Ma
Inference Authors: ['Jiaxin Wang' 'Yilong Ma'] Causal inference originated from research in disciplines such as economics and biostatistics. With the development of artificial intelligence, causal inference is experiencing a resurgence of vitality, accompanied by new challenges. One particularly critical issue is the…
Erica E. M. Moodie, David A. Stephens
Causality is a subject of philosophical debate and a central scientific issue with a long history. In the statistical domain, the study of cause and effect based on the notion of 'fairness' in comparisons dates back several hundred years, and yet statistical concepts and developments that form the area of causal…
Yu Yin, Dezhong Yao
The main concept behind causality involves both statistical conditions and temporal relations. However, current approaches to causal inference, focusing on the probability vs. conditional probability contrast, are based on model functions or parametric estimation. These approaches are not appropriate when addressing…
Tatsuji Takahashi, Kuratomo Oyo, Akihiro Tamatsukuri, Kohki Higuchi
We view observational causal induction as a statistical independence test under rarity assumption. This paper complements the two-stage theory of causal induction proposed by 20 with a computational analysis. We show that their dual-factor heuristic (DFH) model has a rational account as the square root of the index of…
Zach Wood-Doughty, Ilya Shpitser, Mark Dredze
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets. While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied. To facilitate…
Jarrett E. K. Byrnes, Laura E. Dee
As ecology tackles progressively larger problems, we have begun to move beyond the scale at which we can conduct experiments to derive causal inferences. Randomized controlled experiments have long been seen as the gold standard for quantifying causal effects in ecological systems. In contrast, observational data…
Authors not listed
High-throughput experimentation (HTE) in materials science generates vast, high-dimensional datasets relating synthesis parameters to material properties. While machine learning (ML) models excel at predicting properties from these parameters, they often fail to distinguish causal drivers from merely correlated…
A. P. Dawid, Monica Musio
We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention ("effects of causes"), and studying whether there is a causal link between the observed exposure and outcome in an individual case ("causes of effects"). For each of these, we introduce and…
Alex E Yuan, Wenying Shou
Complex ecosystems are challenging to understand as they often defy manipulative experiments for practical or ethical reasons. In response, several fields have developed parallel approaches to infer causal relations from observational time series. Yet these methods are easy to misunderstand and often controversial.…
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
Otto Tabell, Niklas Moser, Otso Ovaskainen, Juha Karvanen
Statistical methods related to causal inference are fundamental in ecological research as ecologists often deal with causal research questions. Consequently, recent years have seen an increase in articles discussing causal inference in ecological context. However, generalizing causal findings across ecological systems…
Authors not listed
Background: Batch reactor process optimization has traditionally relied on Analysis of Variance (ANOVA) for factor effect quantification. However, Structural Equation Modeling (SEM) and machine learning (ML) offer complementary mechanistic and predictive capabilities that remain underexplored in chemical engineering…
Authors not listed
Meteorological normalization is a key concept in studying anthropogenic effects on air pollutant concentrations and its temporal trends. While apparently successful in revealing anthropogenic effects and often used, there are downsides to the methods and limitations which should be taken into account when using it.…
Umberto Lucia, Giulia Grisolia
Causality is the relationship between causes and effects. Following Relativity, any cause of an event must always be in the past light cone of the event itself, but causes and effect must always be related to some interactions. In this paper, causality is developed as a consequence of the analysis of the Einstein…
Martin Robinson, Alan Bond, Alexandr Simonov, Jie Zhang + 1 more
Recently, we have introduced the use of techniques drawn from Bayesian statistics to recover kinetic and thermodynamic parameters from voltammetric data, and were able to show that the technique of large amplitude ac voltammetry yielded significantly more accurate parameter values than the equivalent dc approach. In…