Causal Inference and Machine Learning in Practice with EconML and CausalML

Syrgkanis, Vasilis; Lewis, Greg; Oprescu, Miruna; Hei, Maggie; Battocchi, Keith; Dillon, Eleanor Wiske; Pan, Jing; Wu, Yifeng; Lo, Paul; Chen, Huigang; Harinen, Totte; Lee, Jeong-Yoon

doi:10.1145/3447548.3470792

Cited by 16 publications

(11 citation statements)

References 4 publications

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“…DOSM development and data analysis were done via Python scripts using the EconML [43], scikit-learn, LightGBM, NumPy, SHAP, and scikit-feature packages.…”

Section: Methodsmentioning

confidence: 99%

Surgery duration: Optimized prediction and causality analysis

et al. 2022

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Accurate estimation of duration of surgery (DOS) can lead to cost-effective utilization of surgical staff and operating rooms and decrease patients’ waiting time. In this study, we present a supervised DOS nonlinear regression prediction model whose accuracy outperforms earlier results. In addition, unlike previous studies, we identify the features that influence DOS prediction. Further, in difference from others, we study the causal relationship between the feature set and DOS. The feature sets used in prior studies included a subset of the features presented in this study. This study aimed to derive influential effectors of duration of surgery via optimized prediction and causality analysis. We implemented an array of machine learning algorithms and trained them on datasets comprising surgery-related data, to derive DOS prediction models. The datasets we acquired contain patient, surgical staff, and surgery features. The datasets comprised 23,293 surgery records of eight surgery types performed over a 10-year period in a public hospital. We have introduced new, unstudied features and combined them with features adopted from previous studies to generate a comprehensive feature set. We utilized feature importance methods to identify the influential features, and causal inference methods to identify the causal features. Our model demonstrates superior performance in comparison to DOS prediction models in the art. The performance of our DOS model in terms of the mean absolute error (MAE) was 14.9 minutes. The algorithm that derived the model with the best performance was the gradient boosted trees (GBT). We identified the 10 most influential features and the 10 most causal features. In addition, we showed that 40% of the influential features have a significant (p-value = 0.05) causal relationship with DOS. We developed a DOS prediction model whose accuracy is higher than that of prior models. This improvement is achieved via the introduction of a novel feature set on which the model was trained. Utilizing our prediction model, hospitals can improve the efficiency of surgery schedules, and by exploiting the identified causal relationship, can influence the DOS. Further, the feature importance methods we used can help explain the model’s predictions.

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“…DOSM development and data analysis were done via Python scripts using the EconML [43], scikit-learn, LightGBM, NumPy, SHAP, and scikit-feature packages.…”

Section: Methodsmentioning

confidence: 99%

Surgery duration: Optimized prediction and causality analysis

et al. 2022

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“…10 However, no causal structures (e.g., in the form of causal graphs) that dictate whether no simultaneous heterogeneity is violated were presented. Syrgkanis et al 13 also postulated an independence condition that is equivalent to no simultaneous heterogeneity, without necessarily restricting to binary falseZ and falseX. However, mechanisms that could render this condition satisfied were similarly not discussed, and no explicit consideration was given to potential implications of non-linear instrument-outcome associations or treatment-outcome effects.…”

Section: Discussionmentioning

confidence: 99%

“…18,19 Finally, the conventional IV estimand known as the Wald estimand (here denoted as falseβIV) is defined as falseβIV=normalcnormalonormalv(Y,Z)normalcnormalonormalv(X,Z). 13,20…”

Section: Methodsmentioning

confidence: 99%

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Average Causal Effect Estimation Via Instrumental Variables: the No Simultaneous Heterogeneity Assumption

et al. 2023

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Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X has a causal effect on an outcome Y . Even if the instrument Z satisfies the three core IV assumptions of relevance, independence, and exclusion restriction, further assumptions are required to identify the average causal effect (ACE) of X on Y . Sufficient assumptions for this include homogeneity in the causal effect of X on Y ; homogeneity in the association of Z with X ; and no effect modification. Methods:We describe the no simultaneous heterogeneity assumption, which requires the heterogeneity in the X -Y causal effect to be mean independent of (i.e., uncorrelated with) both Z and heterogeneity in the Z -X association. This happens, for example, if there are no common modifiers of the X -Y effect and the Z -X association, and the X -Y effect is additive linear. We illustrate the assumption of no simultaneous heterogeneity using simulations and by re-examining selected published studies. Results: Under no simultaneous heterogeneity, the Wald estimand equals the ACE even if both homogeneity assumptions and no effect modification (which we demonstrate to be special cases ofand therefore stronger than-no simultaneous heterogeneity) are violated. Conclusions:The assumption of no simultaneous heterogeneity is sufficient for identifying the ACE using IVs. Since this assumption is weaker than existing assumptions for ACE identification, doing so may be more plausible than previously anticipated.

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“…This paper also contributes to a set of works that apply machine learning tools to casual inference. This includes Chernozhukov et al (2016), Belloni et al (2017), Semenova and Chernozhukov (2017), Chernozhukov et al (2018b), Syrgkanis et al (2019), Fan et al (2020), Tan (2020), etc. Our work distinguish this literature in the following two ways.…”

Section: Related Literaturementioning

confidence: 99%

Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data

Ning,

Peng,

Tao

2020

Preprint

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This paper proposes a doubly robust two-stage semiparametric differencein-difference estimator for estimating heterogeneous treatment effects with high-dimensional data. Our new estimator is robust to model miss-specifications and allows for, but does not require, many more regressors than observations. The first stage allows a general set of machine learning methods to be used to estimate the propensity score. In the second stage, we derive the rates of convergence for both the parametric parameter and the unknown function under a partially linear specification for the outcome equation. We also provide bias correction procedures to allow for valid inference for the heterogeneous treatment effects. We evaluate the finite sample performance with extensive simulation studies. Additionally, a real data analysis on the effect of Fair Minimum Wage Act on the unemployment rate is performed as an illustration of our method. An R package for implementing the proposed method is available on Github. 1

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Causal Inference and Machine Learning in Practice with EconML and CausalML

Cited by 16 publications

References 4 publications

Surgery duration: Optimized prediction and causality analysis

Surgery duration: Optimized prediction and causality analysis

Average Causal Effect Estimation Via Instrumental Variables: the No Simultaneous Heterogeneity Assumption

Doubly Robust Semiparametric Difference-in-Differences Estimators with High-Dimensional Data

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