Targeted Learning: Causal Inference for Observational and Experimental Data

By M. J. van der Laan, Sherri Rose. 2011

1,408 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Education, Management Science and Operations Research, Statistics and Probability, Causal Inference & Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Susan Athey
2. Victor Chernozhukov
3. Stefan Wager
4. Christian Hansen
5. D. Chetverikov
6. Alexandre Belloni
7. Guido W. Imbens
8. Whitney K. Newey

In Education

1. Edward H. Kennedy
2. Susan Athey
3. Guido W. Imbens
4. Stefan Wager
5. Iv'an D'iaz
6. Nima S. Hejazi
7. Victor Chernozhukov
8. Whitney K. Newey

In Management Science and Operations Research

1. Stefan Wager
2. Susan Athey
3. Victor Chernozhukov
4. Qingyuan Zhao
5. Guido W. Imbens
6. David A. Hirshberg
7. Edward H. Kennedy
8. D. Chetverikov

In Statistics and Probability

1. Stefan Wager
2. Victor Chernozhukov
3. Susan Athey
4. Whitney K. Newey
5. Guido W. Imbens
6. Christian Hansen
7. Edward H. Kennedy
8. Nathan Kallus

In Causal Inference & Discovery

1. Stefan Wager
2. Susan Athey
3. Victor Chernozhukov
4. Guido W. Imbens
5. Whitney K. Newey
6. Edward H. Kennedy
7. James M. Robins
8. Christian Hansen

Citations in the corpus

• Double/De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers (2018, background): “Targeted maximum likelihood, Van Der Laan and Rubin (2006), based on machine learners has been considered by Van der Laan and Rose (2011) and large sample theory given by Luedtke and Van Der Laan (2016), Toth and van der Laan (2016), and Zheng et al. (2016).”
• Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, methodology): “…an augmented variable selection scheme to avoid this effect, whereas Belloni et al. (2017), Chernozhukov et al. (2017), Farrell (2015) and Van der Laan and Rose (2011) built on the work of Robins et al. (1994, 1995) and discussed how a doubly robust approach to average treatment effect…”
• Double/Debiased Machine Learning for Treatment and Causal Parameters (2016, methodology): “th nuisance parameters estimated by conventional nonparametric estimators such as kernels or series. See, for example, the important work by [14], [50], [40], [54], [2], [41], [49], [38], [15], [19], [53], and [1]. The major point of departure from the present work and this traditional work is that ”
• Double machine learning for treatment and causal parameters (2016, neutral): “See, for example, the important work by [14], [50], [40], [54], [2], [41], [49], [38], [15], [19], [53], and [1].”
• High-dimensional econometrics and regularized GMM (2018, background): “This idea also plays a key role in semiparametric and targeted learning theory; see, for example, [2], [94], [112], [105], and [111].”
• Faster Rates for Policy Learning (2017, methodology): “(17) Observe that above Ḟπ(V(π)) only enters fπ as a multiplicative constant, and therefore an estimating equation-based estimator or TMLE for V(π) can be asymptotically linear for V(π) without estimating Ḟπ.”
• Bias-Reduced Doubly Robust Estimation (2015, methodology): “Finally, we consider two TMLEs μ̂TMLE and μ̂TMLE-SL (van der Laan and Rose 2011) based on quasi-log-likelihood loss functions, where μ̂TMLE uses ordinary least squares as an initial estimate for the conditional mean outcome, whereas μ̂TMLE-SL uses a super learner (van der Laan and Rose 2011, chap.”
• Semiparametric doubly robust targeted double machine learning: a review (2022, methodology): “…models, e.g., Bickel et al. [1993], van der Vaart [2000], van der Vaart [2002], van der Laan and Robins [2003], Tsiatis [2006], Kosorok [2008], van der Laan and Rose [2011], Petersen and van der Laan [2014], Kennedy [2016], Chernozhukov et al. [2018], Kennedy [2018], Díaz [2020], and Hines et…”