Humans know how to reason based on cause and effect, but these are not enough to draw conclusions due to imperfect information and uncertainty. To address this, humans reason combining causal models with probabilistic information. The theory modeling causality and probability is known as Causal Bayes Nets - CBNs. We adopt a logic programming framework and methods to model our functional description of CBNs building on its many strengths and advantages. ACORDA is a declarative prospective logic programming system that simulates human reasoning in multiple steps into the future, but is not equipped to deal with probabilistic theory. P-log is a declarative logic programming language that can reason with probabilistic models. Integrated with P-log, ACORDA becomes ready to deal with uncertainty problems we face on a daily basis. We show how the integration of ACORDA and P-log has been accomplished, and present cases of daily life examples that ACORDA can help people to reason about.