ObjectiveThis study aimed to explore the bidirectional causal relationship between periodontal disease-related phenotype (PDRP) and knee osteoarthritis (KOA) in a European population using a two-sample Mendelian Randomization (MR) approach.MethodsWe leveraged publicly available GWAS summary statistics for PDRP (n = 975) and KOA (n = 403,124), assessing their roles as both exposures and outcomes. Our comprehensive MR analysis employed various methods, including inverse variance weighting (IVW), weighted median, Egger regression, simple mode, and weighted mode, to enhance the robustness of our findings. To ensure the reliability of our instrumental variables, we implemented a rigorous screening process based on p-values and F-values, utilized Phenoscanner to investigate potential confounders, and conducted sensitivity analyses.ResultsOur analysis identified five SNPs associated with PDRP and three SNPs with KOA, all surpassing the genome-wide significance threshold, as instrumental variables. The IVW method demonstrated a significant causal relationship from PDRP to KOA (beta = 0.013, SE = 0.007, P = 0.035), without evidence of directional pleiotropy (MR-Egger regression intercept = 0.021, P = 0.706). No support was found for reverse causality from KOA to PDRP, as further MR analyses yielded non-significant P-values. Additionally, funnel plots and Cochran’s Q test detected no significant heterogeneity or directional pleiotropy, confirming the robustness of our results. In multivariate analysis, when considering smoking, alcohol consumption, BMI collectively no direct causal relationship between KOA and PDRP. Conversely, smoking and higher BMI were independently associated with an increased risk of KOA.ConclusionIn conclusion, our analysis revealed no direct causal relationship from KOA to PDRP. However, a causal relationship from PDRP to KOA was observed. Notably, when adjusting for potential confounders like smoking, alcohol intake, and BMI, both the causal connection from PDRP to KOA and the inverse relationship were not substantiated.

Causal AI Market size was valued at USD 11.77 Million in 2024 and is projected to reach USD 256.73 Million by 2031, growing at a CAGR of 47.1% during the forecast period 2024-2031.

Causal AI also known as causal artificial intelligence is a significant innovation in the fields of artificial intelligence and machine learning that focuses on identifying and harnessing cause-and-effect linkages in data. Traditional AI models generally use correlation-based methods to detect patterns and generate predictions. While these methods can be quite useful in specific applications, they frequently fall short in situations where understanding the underlying causal mechanisms is critical. Causal AI overcomes this issue by incorporating principles from causal inference, a branch of statistics and philosophy that investigates how to infer causal correlations from data.

Causal AI is a huge leap in the field of artificial intelligence allowing us to go beyond correlation to discover the true drivers of observed occurrences. Its applications are broad and diverse including healthcare, finance, marketing, policymaking, operations, education, the environment, and social sciences. Causal AI improves decision-making and allows for the development of focused solutions to meet difficult situations by offering a richer grasp of causality.

Causal AI (Artificial Intelligence) has the potential to change a wide range of domains by providing more precise and actionable insights than typical machine learning models. Causal AI differs from traditional AI in that it focuses on understanding the cause-and-effect relationships underlying data rather than correlations and patterns. This change from correlation to causation is a huge step forward with the potential to improve decision-making processes make better forecasts, and maximize outcomes in a variety of industries including healthcare, finance, marketing, and others.

This dataset is about book subjects and is filtered where the books is Statistical models for causal analysis, featuring 10 columns including authors, average publication date, book publishers, book subject, and books. The preview is ordered by number of books (descending).

Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH4 level, and the CO2 level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly.

https://arxiv.org/pdf/2305.18793 It will also appear at Chapman & Hall.

The causal link between monetary variables and output is one of the most studied issues in macroeconomics. One puzzle from this literature is that the results of causality tests appear to be sensitive with respect to the sample period that one considers. As a way of overcoming this difficulty, we propose a method for analysing Granger causality which is based on a vector autoregressive model with time-varying parameters. We model parameter time-variation so as to reflect changes in Granger causality, and assume that these changes are stochastic and governed by an unobservable Markov chain. When applied to US data, our methodology allows us to reconcile previous puzzling differences in the outcome of conventional tests for money-output causality.

Datasets as well as R and Python code of the empirical examples in the book "Causal Analysis" by Martin Huber (2023), published by MIT Press.

Cause-effect is a two dimensional database with two-variable cause-effect pairs chosen from the different datasets created by Max-Planck-Institute for Biological Cybernetics in Tuebingen, Germany.

Size: 83 datasets of various sizes

Number of features: 2 in every datasets

Ground truth: avalaible for every dataset

Type of Graph: directed

Extension of the datasets used in CauseEffectPairs task. Each dataset consists of samples of a pair of statistically dependent random variables, where one variable is known to cause the other one. The task is to identify for each pair which of the two variables is the cause and which one the effect, using the observed samples only

More information about the dataset is contained in causal_description.html file.

Reference

J. M. Mooij, J. Peters, D. Janzing, J. Zscheischler, B. Schoelkopf: “Distinguishing cause from effect using observational data: methods and benchmarks”, Journal of Machine Learning Research 17(32):1-102, 2016

The null hypotheses () were as follows: volatility (V) did not cause (in the Granger sense) financial press pessimism (B) (, left panel), financial press did not cause volatility (, right panel). “All” indicates all variables included in the test and “*” indicates that the null is rejected at the 5% significance level.

This paper develops a simple sequential multiple-horizon non-causation test strategy for trivariate VAR models (with one auxiliary variable). We apply the test strategy to a rolling window study of money supply and real income, with the price of oil, the unemployment rate and the spread between the Treasury bill and commercial paper rates as auxiliary processes. Ours is the first study to control simultaneously for common stochastic trends, sensitivity of test statistics to the chosen sample period, null hypothesis over-rejection, sequential test size bounds, and the possibility of causal delays. Evidence suggests highly significant direct or indirect causality from M1 to real income, in particular through the unemployment rate and M2 once we control for cointegration.

Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. While they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analyzing time-series cross-sectional data. We fill this methodological gap. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated and matched control observations have similar covariate values. Assessing the quality of matches is done by examining covariate balance. Finally, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator, accounting for a time trend. We illustrate the proposed methodology through simulation and empirical studies. An open-source software package is available for implementing the proposed methods.

The programs replicate figures from "On the Role of the Zero Conditional Mean Assumption for Causal Inference in Linear Models", by Crudu, Knaus, Mellace, and Smits. Please see the ReadMe file for additional details.

If an experimental treatment is experienced by both treated and control group units, tests of hypotheses about causal effects may be difficult to conceptualize let alone execute. In this paper, we show how counterfactual causal models may be written and tested when theories suggest spillover or other network-based interference among experimental units. We show that the ``no interference'' assumption need not constrain scholars who have interesting questions about interference. We offer researchers the ability to model theories about how treatment given to some units may come to influence outcomes for other units. We further show how to test hypotheses about these causal effects, and we provide tools to enable researchers to assess the operating characteristics of their tests given their own models, designs, test statistics, and data. The conceptual and methodological framework we develop here is particularly applicable to social networks, but may be usefully deployed whenever a researcher wonders about interference between units. Interference between units need not be an untestable assumption; instead, interference is an opportunity to ask meaningful questions about theoretically interesting phenomena.

Dataset includes structure and values of a causal model for Training Quality in nuclear power plants. Each entry refers to a piece of evidence supporting causality of the Training Quality causal model. Includes bibliographic information, context-specific text from the reference, and three weighted values; (M1) credibility of reference, (2) causality determined by the author, and (3) analysts confidence level. (M1, M2, and M3) Weight metadata are based on probability language from Intergovernmental Panel on Climate Change (IPCC), Climate Change 2001: Synthesis Report. The language can be found in the “Summary for Policymakers” section, in the PDF format. Weight Metadata: LowerBound_Probability, UpperBound_Probability, Qualitative Language 0.99, 1, Virtually Certain 0.9, 0.99, Very Likely 0.66, 0.9, Likely 0.33, 0.66, Medium Likelihood 0.1, 0.33, Unlikely 0.01, 0.1, Very Unlikely 0, 0.01, Extremely Unlikely

In studies of civil strife, the ecological fallacy seems to befall all large-$n$ studies and thus there has been a big push, by several researchers, in recent years to gather disaggregated, spatially explicit data. However, while such efforts are heroic and are likely to lead to better information, we find that the resulting data can not be analysed in conventional ways, if the estimation of causal effects is the goal. The reason is that such data brings about other dangers: the violation of the Stable Unit Treatment Value Assumption (SUTVA). To be specific, one ``treated'' group's enemy could hardly be its control. We get around this problem by changing the causal effect of interest and by carefully re-aggregating the lower level data so as to preserve its most salient information. Restricting our analysis to groups that are excluded from power, we find some tentative evidence that such groups are less likely to engage in conflict if they are more spatially integrated with other groups.

Basic statistics for original data.

Causality and determinism in economics is a book. It was written by S. A. Drakopoulos and published by University of Aberdeen Dept of Economics in 1992.

This dataset includes past causalities and their categories to connect similar past and present causalities. We report how to use this dataset in the following papers.

Ryohei Ikejiri, Yasunobu Sumikawa: "Developing world history lessons to foster authentic social participation by searching for historical causation in relation to current issues dominating the news". Journal of Educational Research on Social Studies 84, 37–48 (2016). (in Japanese).

Yasunobu Sumikawa and Ryohei Ikejiri, "Mining Historical Social Issues", Intelligent Decision Technologies, Smart Innovation, IDT'15, Systems and Technologies, Vol. 39, Springer, pp. 587--597, 2015.

This dataset is based on some textbooks that are popular ones in Japanese high-school. We first collect past causalities by referencing the textbooks. We then select the causalities if they can be useful for considering solutions for present social issues. To enhance the analogy, we describe each causality in three kinds of texts: background including problems, solution ways, and their results. From the selected causalities and an Encyclopedia of Historiography, we define categories for them. Finally, the created dataset contains 138 past causalities and 13 categories. Each past causality has more than one categories.

File contents:

• FAQ data (*.csv)
  1. historical_causalities_data.tsv: Detail of stored causalities.
  2. historical_causalities_regions.tsv: Regions where the causalities happened.
  3. historical_causalities_categories.tsv: Categories of the causalities.


• Statistics (Statistics.tsv)

  Results of statistical analyses for the dataset. We used Calinski and Harabaz method, mutual information, Jaccard Index, TF-IDF+JS divergence, and Meta-data Similarity that counts how many common categories two causalities share in order to measure qualities of the dataset.

Grants: JSPS KAKENHI Grant Number 26750076 and 17K12792

In the era of big data, the increasing availability of huge data sets can paradoxically be harmful when our causal inference method is designed to search for a causal model that is faithful to our data. Under the commonly made Causal Faithfulness Assumption, we look for patterns of dependencies and independencies in the data and match them with causal models that imply the same patterns. However, given enough data, we start picking up on the fact that everything is ultimately connected. These interactions are not normally picked up in small samples. The only faithful causal model in the limit of a large number of samples (the large-sample limit) therefore becomes the one where everything is connected. Alas, we cannot extract any useful causal information from a completely connected structure without making additional (strong) assumptions. We propose an alternative approach (RoCELL) that replaces the Causal Faithfulness Assumption with a prior that reflects the existence of many "weak" (irrelevant) and "strong" interactions. RoCELL outputs a posterior distribution over the target causal effect estimator that leads to good estimates even in the large-sample limit.

The energy consumption-growth nexus has been widely studied in the empirical literature, though results have been inconclusive regarding the direction, or even the existence, of causality. These inconsistent results can be explained by two important limitations of the literature. First, the use of bivariate models, which fail to detect more complex causal relations, or the ad hoc approach to selecting variables in a multivariate framework; and, second, the use of linear causal models, which are unable to capture more complex nonlinear causal relationships. In this paper, we aim to overcome both limitations by analysing the energy consumption-growth nexus using a Flexible Fourier form due to [1] The analysis focuses on the US over the period 1949 to 2014. From our results we can conclude that, where the linear methodology supports the neutrality hypothesis (no causality between energy consumption and growth), the Flexible Fourier form points to the existence of causality from energy consumption to growth. This is contrary to the linear analysis, suggesting that lowering energy consumption would adversely affect US economic growth. Thus, by employing the Flexible Fourier form we find the conclusions can be quite different.