A campaign active since last November has been targeting Python developers building Telegram bots with trojanized Pyrogram ...

StatsPAI is for empirical researchers who would normally jump between Stata, R, and Python. Its goal is to make common Stata/R econometrics and causal-inference workflows feel native in Python: load a ...

For decades, artificial intelligence has excelled at spotting patterns in data. Machine learning models can predict customer behavior, forecast market trends, or identify medical risks with high ...

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...

In an era where data-driven decision-making dominates the business landscape, traditional AI has excelled at predicting outcomes based on past occurrences. Yet, as our challenges grow in complexity, ...

In addition to efficient statistical estimators of a treatment’s effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and ...

For decades, causal inference methods have found wide applicability in the social and biomedical sciences. As computing systems start intervening in our work and daily lives, questions of ...