Q2: As far as I know there are 2 good ways to test for causality. It is done by the design of the study. Two designs work here:

• In a situation where you can control ALL independent variables (usually in experiments). This makes sure that A is causing B, and there is no C causing the effect (and also not the reverse effect; B causing A).
• In longitudinal studies.

Q1: Rubin's Causal Model: http://en.wikipedia.org/wiki/Rubin_causal_model

Q3: There is no "trick" to seeing whether the statistics are good. I find the most important questions you should ask yourself:

• Who did the research?
• What is to be gained by the researcher with a favorable outcome?

Q2: As far as I know there are 2 good ways to test for causality. It is done by the design of the study. Two designs work here:

• In a situation where you can control ALL independent variables (usually in experiments). This makes sure that A is causing B, and there is no C causing the effect (and also not the reverse effect; B causing A).
• In longitudinal studies.

Q1: Rubin's Causal Model: http://en.wikipedia.org/wiki/Rubin_causal_model

Q3: There is no "trick" to seeing whether the statistics of one single article are good. I find the most important questions you should ask yourself:

• Who did the research?
• What is to be gained by the researcher with a favorable outcome?