Every decision tree has an influential variable
Ryan O'Donnell, Michael Saks, Oded Schramm, Rocco A. Servedio

TL;DR
This paper establishes a fundamental inequality relating the variance, influence, and probability of variables in decision trees, leading to new lower bounds on the depth and influence for various decision tree models and applications.
Contribution
It proves a general inequality connecting variance, influence, and read probability in decision trees, extending influence lower bounds to randomized and non-boolean decision trees.
Findings
Proves variance-influence inequality for decision trees under arbitrary product measures.
Derives lower bounds on decision tree depth based on variable influence.
Provides a simplified proof of lower bounds on randomized query complexity for monotone graph properties.
Abstract
We prove that for any decision tree calculating a boolean function , \[ \Var[f] \le \sum_{i=1}^n \delta_i \Inf_i(f), \] where is the probability that the th input variable is read and is the influence of the th variable on . The variance, influence and probability are taken with respect to an arbitrary product measure on . It follows that the minimum depth of a decision tree calculating a given balanced function is at least the reciprocal of the largest influence of any input variable. Likewise, any balanced boolean function with a decision tree of depth has a variable with influence at least . The only previous nontrivial lower bound known was . Our inequality has many generalizations, allowing us to prove influence lower bounds for randomized decision trees, decision trees on…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Machine Learning and Algorithms · Markov Chains and Monte Carlo Methods
