The recent spotlight on large language models (LLMs, such as Chatgpt) seems to have divided the public into two parties, debating whether they will take over human governance and control the world in the coming era of artificial intelligence, or whether they simply are tools to boost our productivity. What we should expect and what we could do in the right way to create reliable, trustworthy and safe AI?
Tag Archives: Causal Inference
Learning Record: Causal Inference [2]
RCT may not be feasible all the time. But does it mean that we are just stuck in the first step of Pearl’s causal ladder? This blog introduces two fundamental methods Backdoor and Front-door Adjustment that can be used to answer interventional and counterfactual queries (the 2nd and 3rd steps in Pearl’s causal ladder) if the causal relationship satisfies certain criteria.
Learning Record: Causal Inference [1]
Correlation, or so-called associational relationship, absolutely should never imply the causation, while it is quite common for even some professional statistists to make this mistake. In fact, the debate between correlation and causation has persisted decades: A part of classical statistists, such as Francis Galton and Karl Pearson, insisted that causation is an “anti-scientific” subject. As a result, related exploration was stalled for many years, and some exciting and gratifying advancements are observed still very recent years.
J. Mao's Tech House 