v0.9.0 The 0.9.x series drops support of Python 3.5.x. Only Python 3.6+ are now supported. Support has also been added for Python 3.8 Cross-fit estimators have been implemented for better causal inference with machine learning. Cross-fit estimators include SingleCrossfitAIPTW, DoubleCrossfitAIPTW, SingleCrossfitTMLE, and DoubleCrossfitTMLE. Currently functionality is limited to treatment and outcome nuisance models only (i.e. no model for missing data). These estimators also do not accept weighted data (since most of sklearn does not support weights) Super-learner functionality has been added via SuperLearner. Additions also include emprical mean (EmpiricalMeanSL), generalized linear model (GLMSL), and step-wise backward/forward selection via AIC (StepwiseSL). These new estimators are wrappers that are compatible with SuperLearner and mimic some of the R superlearner functionality. Directed Acyclic Graphs have been added via DirectedAcyclicGraph. These analyze the graph for sufficient adjustment sets, and can be used to display the graph. These rely on an optional NetworkX dependency. AIPTW now supports the custom_model optional argument for user-input models. This is the same as TMLE now. zipper_plot function for creating zipper plots has been added. Housekeeping: bound has been updated to new procedure, updated how print_results displays to be uniform, created function to check missingness of input data in causal estimators, added warning regarding ATT and ATU variance for IPTW, and added back observation IDs for MonteCarloGFormula Future plans: TimeFixedGFormula will be deprecated in favor of two estimators with different labels. This will more clearly delineate ATE versus stochastic effects. The replacement estimators are to be added