Maybe the easiest way to understand UDT and TDT is:

• UDT = EDT without updating on sensory inputs, with "actions" to be understood as logical facts about the agent's outputs
• TDT = CDT with "causality" to be understood as Pearl's notion of causality plus additional arrows for logical correlations

Comparing UDT and TDT directly, the main differences seem to be that UDT does not do Bayesian updating on sensory inputs and does not make use of causality. There seems to be general agreement that Bayesian updating on sensory inputs is wrong in a number of situations, but disagreement and/or confusion about whether we need causality. Gary Drescher put it this way:

Plus, if you did have a general math-counterfactual-solving module, why would you relegate it to the logical-dependency-finding subproblem in TDT, and then return to the original factored causal graph? Instead, why not cast the whole problem as a mathematical abstraction, and then directly ask your math-counterfactual-solving module whether, say, (Platonic) C's one-boxing counterfactually entails (Platonic) $1M? (Then do the argmax over the respective math-counterfactual consequences of C's candidate outputs.)

(Eliezer didn't give an answer. ETA: He did answer a related question here.)