Measurement bias and effect restoration in causal inference

By Manabu Kuroki, Judea Pearl. 2014

294 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Causal Effect Inference with Deep Latent-Variable Models (2017, neutral): “For future work, we plan to employ the expanding set of tools available for latent variables models (e.g. Kingma et al. [28], Tran et al. [51], Maaløe et al. [35], Ranganath et al. [44]), as well as to further explore connections between method of moments approaches such as Anandkumar et al. [5] wit”
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Alternative methods for dealing with hidden confounders in the static setting involve using proxy variables as noisy substitutes for latent confounders (Lash et al., 2014; Kuroki & Pearl, 2014; Louizos et al., 2017; Lee et al., 2018).”
Deconfounding Reinforcement Learning in Observational Settings (2018, methodology): “Despite this, identiﬁcation is possible in both our model and the one described by Louizos et al. (2017) under speciﬁc conditions (Louizos et al., 2017; Kuroki and Pearl, 2014; Miao et al., 2018; Pearl, 2012; Allman et al., 2009).”
Multi-cause causal inference with unmeasured confounding and binary outcome. (2019, background): “996; Hernán and Robins, 2006; Wang and Tchetgen Tchetgen, 2018), causal structure learning (e.g., Drton and Maathuis, 2017), invariant prediction (e.g., Peters et al., 2016), negative controls (e.g., Kuroki and Pearl, 2014; Miao et al., 2018) and sensitivity analysis (e.g., Cornﬁeld et al., 1959). 1”
A confounding bridge approach for double negative control inference on causal effects (2018, unknown): “Confounder proxies used by Miao, Geng, et al. (2018) and Kuroki and Pearl (2014) can be viewed as special negative controls in our framework.”
Adjusting for Confounding with Text Matching (2020, methodology): “Johansson, Shalit, and Sontag (2016) and Louizos et al. (2017) consider learned representations optimized for causal inference.”
Multiple Causes: A Causal Graphical View (2019, methodology): “Then Assumption 1.1 reduces to the null proxy assumption (Kuroki & Pearl, 2014; Miao et al., 2018; D’Amour, 2019).”
Challenges of Using Text Classifiers for Causal Inference (2018, background): “Recent work has also considered how multiple independent proxies of A could allow identification without any data on p(A∗, A) (Kuroki and Pearl, 2014).”
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning (2018, background): “If such latent confounders are not appropriately accounted for, then the estimated ITE will be subject to confounding bias, making it impossible to estimate the effect of the intervention on the outcome without bias [1, 2].”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, unknown): “g, socioeconomic status) (Kuroki and Pearl, 2014; Oktay et al., 2019).”
Robust Text Classification in the Presence of Confounding Bias (2016, background): “In future work, we will consider the case where we only have a noisy estimate of Z at training time (Kuroki and Pearl 2014), as well as the case where Z is a vector of variables.”
Policy Evaluation with Latent Confounders via Optimal Balance (2019, methodology): “As in our problem setup, much of this work is based on the model of using an identified latent variable model for the proxies [9, 27, 34, 37, 43].”