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What are the state-of-the-art review papers on causal inference? 

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State-of-the-art review papers on causal inference encompass various domains like software engineering and AI models. In software engineering, recent work has focused on applying statistical causal inference methods to estimate causal effects from observational data, with a review of 32 papers highlighting the fragmented research community . On the other hand, in the realm of AI models, causal modeling and inference methods have emerged as powerful tools to enhance trustworthiness, addressing issues like generalization, fairness, and interpretability. A review paper aims to provide an overview of causal methods developed to improve the trustworthiness of AI models, encouraging further research in causality-based solutions for trustworthy AI .

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"A Review of the Role of Causality in Developing Trustworthy AI Systems" discusses state-of-the-art causal methods for improving trustworthy AI, serving as a valuable review paper on causal inference.
Not addressed in the paper.
State-of-the-art review papers on causal inference in Image Captioning are surveyed, categorizing models based on adjustment methods to address dataset bias in training for advanced performance.
Not addressed in the paper.
Proceedings ArticleDOI
Jungeun Kim, Junyeong Kim 
05 Feb 2023
Not addressed in the paper.

Related Questions

What is the cutting edge research on causal inference?5 answersCutting-edge research on causal inference spans various domains. Recent advancements include the development of causal estimands in recurrent event settings, clarifying interpretations of classical statistical estimands in the presence of competing events. Additionally, the introduction of a method utilizing genetic data from parents and offspring allows for causal inferences immune to confounding, enhancing power and localization compared to traditional tests. In cognitive neuroscience, emphasis is placed on the importance of causal knowledge for interventions, connecting inference methods with scientific goals and challenges. Furthermore, a novel framework combining deep learning and double machine learning enables the estimation of causal effects for unobserved treatment combinations, showcasing superior performance in identifying optimal treatment combinations. These advancements highlight the ongoing progress in enhancing causal inference methodologies across diverse fields.
How to do causal inference with machine learning models?4 answersCausal inference with machine learning models can be done by combining predictive machine learning models with analytical estimations of average treatment effects. This approach allows for the utilization of any predictive model for causal inference, making it adaptable to existing systems. The process involves estimating the average treatment effect of an intervention on predictors, determining the causal relationship between the intervention and a wide range of variables. Artificial samples are then created and evaluated using the predictive model to link interventions and outcomes, providing measurements of uncertainty. Simulations are performed using the predictive model to compute measurements of confidence and compare the effects of specific treatments. This approach can also be adapted to privacy-preserving federated learning environments, where training data is distributed across multiple datasets.
How is causal inference used in deep learning?4 answersCausal inference is used in deep learning to understand the causal relationships and estimate the effect size in complex systems. It helps identify risk factors, disease mechanisms, and candidate therapeutics for diseases. Deep learning models often fit undesired dataset bias in training, and causal inference can be used to uncover causalities among key factors and pursue the desired causal effect without bias. Additionally, causal inference is used in deep learning to estimate the causal effect of treatment combinations in large-scale online platforms, even when only a small subset of combinations is observed. It is also applied to develop causality-aware coverage criteria for testing the correctness and robustness of deep neural networks, considering how neurons jointly make predictions. Causal inference in deep learning is achieved through various methods, including deep neural networks, double machine learning, and structural causal models.
Is causal inference used in the field of catalytic science?5 answersCausal inference is used in various fields, including catalytic science. The move from causal to catalytic models has brought major breakthroughs in chemistry and biology. In the field of catalytic science, the use of simple cause-effect notions in psychology is recommended to be overcome, and catalytic thinking is suggested as a better approach. Additionally, the study of how actions, interventions, or treatments affect outcomes of interest, which is a key aspect of causal inference, has been applied in the context of observational studies to deliver valuable insights in data science. Therefore, causal inference plays a role in catalytic science, both in terms of theoretical models and practical applications.
What are temporal causal inference techniques?5 answersTemporal causal inference techniques involve analyzing the relationships between variables in time series data to determine cause and effect. These techniques aim to understand the temporal patterns and underlying causal mechanisms in complex dynamical systems. Time-series representations such as discrete Fourier and wavelet transforms are commonly used to study the temporal structure of these systems. Traditional methods often assume a predefined time lag between cause and effect, but this may not accurately capture the true lag in real-world applications. Recent research has focused on developing methods that can estimate the lag among different time series variables from the data itself, allowing for more accurate causal inference. These techniques have been shown to be effective in learning the temporal causal relationships and can provide valuable insights into the interactive scheme of temporal variables.
Can machine learning be used for causal inference?5 answersMachine learning can be used for causal inference. It allows researchers to estimate causal effects and uncover heterogeneity in these effects. By combining machine learning with causal inference, potential biases in estimating causal effects can be addressed, and sources of effect heterogeneity can be identified. This is particularly important for generalizing findings to populations beyond those under study. Machine learning algorithms, such as Bayesian Additive Regression Trees (BART), can be used to model the relationship between outcomes, covariates, and a treatment to estimate causal effects. Additionally, machine learning can be incorporated into causal inference to estimate the causal mean and address challenges such as slower convergence and complexity of the nuisance model. The use of machine learning in causal inference enables researchers to make more principled estimations of causal effects and improve the accuracy of their findings.

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