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ning icml 97 3201 3210 2019 arxiv february 2019 github abstract in the context of individual level causal inference we study the problem of predicting whether someone will respond or not to a treatment based on their features and past examples of features treatment indicator e g drug no drug and a binary outcome e g recovery from disease as a classification task the problem is made difficult by not knowing the example outcomes under the opposite treatment indicators we assume the effect is monotonic as in advertising s effect on a purchase or bail setting s effect on reappearance in court either it would have happened regardless of treatment not happened regardless or happened only depending on exposure to treatment predicting whether the latter is latently the case is our focus while previous work focuses on conditional average treatment effect estimation formulating the problem as a classification task rather than an estimation task allows us to develop new tools more suited to this problem by leveraging monotonicity we develop new discriminative and generative algorithms for the responder classification problem we explore and discuss connections to corrupted data and policy learning we provide an empirical study with both synthetic and real datasets to compare these specialized algorithms to standard benchmarks c optimization and personalization causal inference 0 confounding robust policy improvement with a zhou conference on neural information processing systems neurips 9289 9299 2018 arxiv may 2018 github abstract we study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding in the data generating process unlike previous approaches that assume unconfoundedness i e no unobserved confounders affected both treatment assignment and outcomes we calibrate policy learning for realistic violations of this unverifiable assumption with uncertainty sets motivated by sensitivity analysis in causal inference our framework for confounding robust policy improvement optimizes the minimax regret of a candidate policy against a baseline or reference status quo policy over an uncertainty set around nominal propensity weights we prove that if the uncertainty set is well specified robust policy learning can do no worse than the baseline and only improve if the data supports it we characterize the adversarial subproblem and use efficient algorithmic solutions to optimize over parametrized spaces of decision policies such as logistic treatment assignment we assess our methods on synthetic data and a large clinical trial demonstrating that confounded selection can hinder policy learning and lead to unwarranted harm while our robust approach guarantees safety and focuses on well evidenced improvement c causal inference 0 removing hidden confounding by experimental grounding with a m puli and u shalit conference on neural information processing systems neurips 10911 10920 2018 arxiv october 2018 github spotlight at neurips 3 5 percnt abstract observational data is being increasingly used as a means for making individual level causal predictions and intervention recommendations the foremost challenge of causal inference from observational data is hidden confounding whose presence cannot be tested in data and can invalidate any causal conclusion experimental data does not stuffer from confounding but is usually limited in both scope and scale we introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data even if the observational data does not fully overlap with the experimental data our method makes strictly weaker assumptions than existing approaches and we prove conditions under which our method yields a consistent estimator we demonstrate our method s efficacy using real world data from a large educational experiment c optimization and personalization causal inference 0 balanced policy evaluation and learning conference on neural information processing systems neurips 8909 8920 2018 arxiv may 2017 abstract we present a new approach to the problems of evaluating and learning personalized decision policies from observational data of past contexts decisions and outcomes only the outcome of the enacted decision is available and the historical policy is unknown these problems arise in personalized medicine using electronic health records and in internet advertising existing approaches use inverse propensity weighting or doubly robust versions to make historical outcome or residual data look like it were generated by a new policy being evaluated or learned but this relies on a plug in approach that rejects data points with a decision that disagrees with the new policy leading to high variance estimates and ineffective learning we propose a new balance based approach that too makes the data look like the new policy but does so directly by finding weights that optimize for balance between the weighted data and the target policy in the given finite sample which is equivalent to minimizing worst case or posterior conditional mean square error our policy learner proceeds as a two level optimization problem over policies and weights we demonstrate that this approach markedly outperforms existing ones both in evaluation and learning which is unsurprising given the wider support of balance based weights we establish extensive theoretical consistency guarantees and regret bounds that support this empirical success c causal inference 0 causal inference with noisy and missing covariates via matrix factorization with x mao and m udell conference on neural information processing systems neurips 6921 6932 2018 arxiv june 2018 github abstract valid causal inference in observational studies often requires controlling for confounders however in practice measurements of confounders may be noisy and can lead to biased estimates of causal effects we show that we can reduce the bias caused by measurement noise using a large number of noisy measurements of the underlying confounders we propose the use of matrix factorization to infer the confounders from noisy covariates a flexible and principled framework that adapts to missing values accommodates a wide variety of data types and can augment many causal inference methods we bound the error for the induced average treatment effect estimator and show it is consistent in a linear regression setting using exponential family matrix completion preprocessing we demonstrate the effectiveness of the proposed procedure in numerical experiments with both synthetic data and real clinical data c optimization and personalization fairness 0 fairness under unawareness assessing disparity when protected class is unobserved with j chen x mao g svacha and m udell acm conference on fairness accountability and transparency facct 339 348 2019 arxiv november 2018 github abstract assessing the fairness of a decision making system with respect to a protected class such as gender or race is complicated by not being able to observe class membership labels due to legal or operational reasons probabilistic models for predicting the protected class based on observed proxy variables such as surname and geolocation for race are sometimes used to impute these missing labels such methods are known to be used by government regulators and have been observed to exaggerate disparities the reason why is unknown as is whether overestimation is always the case we decompose the bias of estimating outcome disparity via an existing threshold based imputation method into multiple interpretable bias sources which explains when over or underestimation occurs we also propose an alternative weighted estimator that uses soft classification rules rather than hard imputation and show that its bias arises simply from the conditional covariance of the outcome with the true class membership we illustrate our results with numerical simulations as well as an application to a dataset of mortgage applications using geolocation as a proxy for race we confirm that the bias of threshold based imputation is generally upward however its magnitude varies strongly with the threshold chosen due to the complex interplay of multiple sources of bias uncovered by our theoretical analysis our new weighted estimator tends to have a negative bias that is much simpler to analyze and reason about c optimization and personalization causal inference 0 interval estimation of individual level causal effects under unobserved confounding with x mao and a zhou international conference on artificial intelligence and statistics aistats 89 2281 2290 2019 arxiv october 2018 abstract we study the problem of learning conditional average treatment effects cate from observational data with unobserved confounders the cate function maps baseline covariates to individual causal effect predictions and is key for personalized assessments recent work has focused on how to learn cate under unconfoundedness i e when there are no unobserved confounders since cate may not be identified when unconfoundedness is violated we develop a functional interval estimator that predicts bounds on the individual causal effects under realistic violations of unconfoundedness our estimator takes the form of a weighted kernel estimator with weights that vary adversarially we prove that our estimator is sharp in that it converges exactly to the tightest bounds possible on cate when there may be unobserved confounders further we study personalized decision rules derived from our estimator and prove that they achieve optimal minimax regret asymptotically we assess our approach in a simulation study as well as demonstrate its application in the case of hormone replacement therapy by comparing conclusions from a real observational study and clinical trial c optimization and personalization causal inference fairness 0 residual unfairness in fair machine learning from prejudiced data with a zhou international conference on machine learning icml 80 2439 2448 2018 arxiv june 2018 abstract recent work in fairness in machine learning has proposed adjusting for fairness by equalizing accuracy metrics across groups and has also studied how datasets affected by historical prejudices may lead to unfair decision policies we connect these lines of work and study the residual unfairness that arises when a fairness adjusted predictor is not actually fair on the target population due to systematic censoring of training data by existing biased policies this scenario is particularly common in the same applications where fairness is a concern we characterize theoretically the impact of such censoring on standard fairness metrics for binary classifiers and provide criteria for when residual unfairness may or may not appear we prove that under certain conditions fairness adjusted classifiers will in fact induce residual unfairness that perpetuates the same injustices against the same groups that biased the data to begin with thus showing that even state of the art fair machine learning can have a bias in bias out property when certain benchmark data is available we show how sample reweighting can estimate and adjust fairness metrics while accounting for censoring we use this to study the case of stop question and frisk sqf and demonstrate that attempting to adjust for fairness perpetuates the same injustices that the policy is infamous for c optimization and personalization causal inference 0 policy evaluation and optimization with continuous treatments with a zhou international conference on artificial intelligence and statistics aistats 84 1243 1251 2018 arxiv february 2018 github finalist best paper of informs 2017 data mining and decision analytics workshop abstract we study the problem of policy evaluation and learning from batched contextual bandit data when treatments are continuous going beyond previous work on discrete treatments previous work for discrete treatment action spaces focuses on inverse probability weighting ipw and doubly robust dr methods that use a rejection sampling approach for evaluation and the equivalent weighted classification problem for learning in the continuous setting this reduction fails as we would almost surely reject all observations to tackle the case of continuous treatments we extend the ipw and dr approaches to the continuous setting using a kernel function that leverages treatment proximity to attenuate discrete rejection our policy estimator is consistent and we characterize the optimal bandwidth the resulting continuous policy optimizer cpo approach using our estimator achieves convergent regret and approaches the best in class policy for learnable policy classes we demonstrate that the estimator performs well and in particular outperforms a discretization based benchmark we further study the performance of our policy optimizer in a case study on personalized dosing based on a dataset of warfarin patients their covariates and final therapeutic doses our learned policy outperforms benchmarks and nears the oracle best linear policy c optimization and personalization causal inference sequential decision making 0 instrument armed bandits international conference on algorithmic learning theory alt 2018 arxiv may 2017 abstract we extend the classic multi armed bandit mab model to the setting of noncompliance where the arm pull is a mere instrument and the treatment applied may differ from it which gives rise to the instrument armed bandit iab problem the iab setting is relevant whenever the experimental units are human since free will ethics and the law may prohibit unrestricted or forced application of treatment in particular the setting is relevant in bandit models of dynamic clinical trials and other controlled trials on human interventions nonetheless the setting has not been fully investigate in the bandit literature we show that there are various and divergent notions of regret in this setting all of which coincide only in the classic mab setting we characterize the behavior of these regrets and analyze standard mab algorithms we argue for a particular kind of regret that captures the causal effect of treatments but show that standard mab algorithms cannot achieve sublinear control on this regret instead we develop new algorithms for the iab problem prove new regret bounds for them and compare them to standard mab algorithms in numerical examples j causal inference applied ml 0 more robust estimation of sample average treatment effects using kernel optimal matching in an observational study of spine surgical interventions with b pennicooke and m santacatterina statistics in medicine 2021 arxiv november 2018 github abstract inverse probability of treatment weighting iptw which has been used to estimate sample average treatment effects sate using observational data tenuously relies on the positivity assumption and the correct speci...