Decision trees with binary split-offs can be modeled using nested and sequential logistic regressions. I feel rather confused about the difference between the two, although I do have some thoughts.

Nested logit seems to be appropriate in situations where

  • Every subjects walks through the entire decision tree
  • There are random effects across the subjects at each level
  • We are interested in within-level variation
  • Covariates are at level-specific

Sequential logit seems to be appropriate in situations where:

  • At every split-off, some subjects are lost whereas others continue
  • We are interested in between-level variation
  • Covariates are subject-specific

In Stata, there are the corresponding commands nlogit and seqlogit. Any comments would be much appreciated.

  • $\begingroup$ I believe the fundamental difference between nested and logit models (NM vs LM) is that NM's include alternative-specific variables (so you have many observations for each individuals: one per alternative) while SM's include sequential choices that, as you said, progressively reduce the number of individuals (but you only have one observation per individual). $\endgroup$ – Federico Tedeschi Jun 29 '18 at 14:44

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