Causal models are crucial for explaining the causal relationships among variables. These models help to understand how various factors interact and influence each other in complex systems. However, it is challenging to find the probabilities related to interventions and conditioning at the same time. Moreover, AI research has focused on two types of models: functional causal models and causal Bayesian networks (CBN). It is simple to calculate the conditional probability of formulas that have interventions using functional causal models, while using CBN shows that there is no explicit reduction or formal definition when finding the probabilities of a formula.
Interventions and conditioning are the key methods in the causal model’s application to study and analyze causal mechanisms. One of the recent applications of interventions is to explain the result of a complex ML system, e.g. It is important to know whether a specific intervention will change patient outcomes in AI-driven healthcare diagnostics. In epidemiology, causal models help to understand the relationships between lifestyle choices and health outcomes, while in economics, these models are used to analyze the impact of changes on market behavior. Such examples show that causal models provide a formal representation of system variables.
Researchers from the Computer Science Department at Cornell University have presented a way to estimate the probability of an interventional formula by making simple but real and independent assumptions. The interventional formula also includes the concept of probability of sufficiency and necessity. When the stated assumptions are true, the estimated probabilities are calculated with the help of observational data, which are useful in cases where conducting experiments is impossible. Moreover, the assumption states that “not only are the equations that define different variables independent, but also the equations that give the values of a variable for different settings of its parents”.
These independence assumptions are useful in identifying the probability of queries in a CBN in a unique way instead of getting a range of values. In this paper, researchers mentioned various works, which include (a) reviewing the formalism of causal models, (b) Interpreting the formulas in CBNs, (c) showing that CBN can be converted into a compatible casual model that satisfies the stated independence assumptions, and (d) showing the simplified and evaluated probabilities of sufficiency and necessity. Moreover, in a functional causal model, some variables have a causal effect on others, which is modeled by a set of structured equations.
In the functional causal model, the causal effect of variables uses a set of structured equations, which is used to split the variables into two sets. The first set is exogenous variables (EVs), where the factors outside the model are used to evaluate their values. The second set is endogenous variables, which uses the first set, EVs, to determine their values. Moreover, EVs may use non-observable factors which may not be known. For example, endogenous variables can be used in an agricultural setting to explain crop produce, fertilizers’ quantity utilized, water usage, etc. While EVs can be used to explain weather conditions (which cannot be changed) and the activity level of pollinators.
In conclusion, researchers introduced a way to find the probability of an interventional formula by making simple but real and independent assumptions. When these assumptions are appropriate, the estimated probabilities can be evaluated with the help of observational data, which are useful in cases where conducting experiments is impossible. Researchers mentioned various works, including a review of the formalism of causal models, interpretation of the formulas in CBNs, etc. In functional causal models, the causal effect of variables uses a set of structured equations, which helps split the variables into two sets: EVs and endogenous variables.
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