Causal inference practitioners are routinely presented with the challenge of wanting to adjust for large numbers of covariates despite limited sample sizes.Collaborative Targeted Maximum Likelihood Estimation(CTMLE)is a general framework for constructing doubly robust semiparametric causal estimators that data-adaptively reduce model complexity in the propensity score in order to optimise a preferred loss function.This stepwise complexity reduction is based on a loss function placed on a strategically updated model for the outcome variable,assessed through cross-validation.New work involves integrating penalized regression methods into a stepwise CTMLE procedure that may allow for a more flexible type of model selection than existing variable selection techniques.