  In  Section 4.6,  we traced the reasons for the development of theories incorporating causality in work on reasoning about action and change. This is not the only area of AI in which causality has emerged. Causality figures in qualitative reasoning about devices; for Herbert Simon’s important work in this area, which goes back to the 1950s, see  Simon 1952;  1977;  Iwasaki &amp; Simon 1986.  Both these traditions are important. But the most robust and highly developed program in AI relating to causality is that of Judea Pearl and his students and associates, which derives from statistical techniques known as structural equation models.   Halpern and Pearl 2001 introduced the idea that causal relations among events could be inferred from these models: Bayesian belief networks could be interpreted as  causal networks.