What statisttical method would help once we are trying to see if parameter A is the cause of B?
as an example is the toxicity of a particular element the cause for a disorder?
Generally a controlled experiment (with specific attributes) is required to establish causation or causal relationship.
You can explore the correlation or association between a continuous or categorical response, respectively, and a factor using analysis of variance (categorical factor) or linear or non-linear regression (continuous factor) or logistic regression and other methods.
See Help > Books > Fitting Linear Models.
As Mark states, there is no substitute for a designed experiment (and some domain knowledge).
Correlation does not necessarily imply causation. Case in point:
The naivity of your question suggests that you might benefit from some training in explanatory modeling. JMP and JMP Training offers such courses.
have applied regression the graph shows no effect on the parameter. i was looking for other method Correlation explains the strenght among the parameters and not the cause. even high correlation doesnt always mean cause other factors could contribute as well
There are many models to choose from. You have not shared the data or the results of the regression analysis so far, so it is difficult to guide you further.
There are empirical models and theoretical models. There are models for testing hypotheses and models for interpolation. Of course, there are many methods for estimating such models. I am sure that the JMP Community can help you if you tell us more about the comparison.
I think the method you are looking for is called "science', and in science we develop theories that help us anticipate the expected causal relationships. Of course, there can be competing theories, or competing causal relationships, and so we conduct experiments to see what the real-world tells us. And here statistics can help us design the experiments so that they are more efficient in the use of resources, and to ensure that if there is a real relationship we are likely to detect it. As part of our analysis of the experiment data we will typically use regression techniques to build statistical models and these will give us more confidence in the selection of competing theories about causation.