The method of the estimation of the probability of an event occurring under the influence of the causal and random effects is considered. Epistemological differences from the traditional approaches to causality are discussed, and a new model of the statistical estimation of the parameters of each effect is proposed. The simple and effective algorithms of the model parameters estimation are presented, and numerical simulations are performed. A practical marketing example is analyzed. The results support the validity of the estimation procedure and open the perspective for the application of the method for various decision making problems, where different causes can yield the same outcome
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...
Two approaches to causal inference in the presence of non-random assignment are presented: The Prope...
We describe and contrast two distinct problem areas for statistical causality: studying the likely e...
This paper describes, in a non‑technical way, the main impact evaluation methods, both experimental ...
A state of the art volume on statistical causality Causality: Statistical Perspectives and Applicati...
Many statistical problems in causal inference involve a probability distribution other than the one ...
Experiments have always been the way to study what the effect is of interventions. Causal inference ...
The capability of large businesses and eCommerce platforms to utilize vast amounts of customer data ...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
This manuscript includes three topics in causal inference, all of which are under the randomization ...
This paper examines different approaches for assessing causality as typically followed in econometri...
This review presents empirical researchers with recent advances in causal inference, and stresses th...
Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Re...
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts th...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...
Two approaches to causal inference in the presence of non-random assignment are presented: The Prope...
We describe and contrast two distinct problem areas for statistical causality: studying the likely e...
This paper describes, in a non‑technical way, the main impact evaluation methods, both experimental ...
A state of the art volume on statistical causality Causality: Statistical Perspectives and Applicati...
Many statistical problems in causal inference involve a probability distribution other than the one ...
Experiments have always been the way to study what the effect is of interventions. Causal inference ...
The capability of large businesses and eCommerce platforms to utilize vast amounts of customer data ...
Providing a thorough treatment on statistical causality, this resource presents a broad collection o...
This manuscript includes three topics in causal inference, all of which are under the randomization ...
This paper examines different approaches for assessing causality as typically followed in econometri...
This review presents empirical researchers with recent advances in causal inference, and stresses th...
Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Re...
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts th...
A concise and self-contained introduction to causal inference, increasingly important in data scienc...
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...
Two approaches to causal inference in the presence of non-random assignment are presented: The Prope...
We describe and contrast two distinct problem areas for statistical causality: studying the likely e...