We discuss causal effect evaluation and causal network learning. First for the causal effect evaluation, we want to evaluate the causal effects of the cause variables on the effect variables. Yule-Simpson paradox means that the association between two variables may be reversed by omitting a third variable, called a confounder. The identifiability of causal effects is discussed when some confounder is unobserved or missing not at random [2]. In medical studies and clinical trials, surrogates and biomarkers are often used to reduce costs or duration when measurement of a true endpoint may be expensive, inconvenient or infeasible in a practical length of time. We present the surrogate paradox that a treatment has a positive effect on the surrogate, and the surrogate has a positive effect on the endpoint, but the treatment may have a negative effect on the endpoint [1]. Many existing criteria of surrogates cannot avoid the surrogate paradox. We propose novel criteria to avoid the surrogate paradox [4, 6].