Statistics and Causal Inference

By Paul Holland. 1985

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias (2020, background): “The so-called fundamental problem of causal inference (Holland, 1986) says that we cannot observe the counterfactual of two different interventions on the same unit, e.”
Causal and anti-causal learning in pattern recognition for neuroimaging (2015, background): “the features in Xenc S are genuine effects of S [12].”
Explainable machine learning in deployment (2019, background): “While the term counterfactual has awell understoodmeaning in the causality literature [28, 45], counterfactual explanations forML”
The Econometrics of Randomized Experiments (2016, background): “The work by Rubin (1973, 1975, 1978) led Holland (1986) [8] to label it the Rubin Causal Model (RCM).”
The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “Because a given unit can only receive one level of the treatment, say Wi, and only the corresponding level of the outcome, Y obs i = Yi(Wi) can be observed, we can never directly observe the causal effects, which is what Holland [1986] calls the “fundamental problem of causal inference.” Estimates o”
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning (2015, background): “In medical and public-policy domains (Murphy et al., 2001; Hirano et al., 2003), it is often hard to run a controlled experiment to estimate the treatment effect, and off-policy There are roughly two classes of approaches to off-policy value evaluation.”
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, methodology): “In the widely used potential outcomes framework we use to formalize our results [Holland, 1986, Imbens and Rubin, 2015, Rosenbaum and Rubin, 1983, Rubin, 1974], a treatment eﬀect is understood as a diﬀerence between two potential outcomes: e.g., would the patient have died is they received the drug…”
Counterfactual Estimation and Optimization of Click Metrics for Search Engines (2014, methodology): “As mentioned earlier, the oﬄine evaluation technique is closely related to causal inference in statistics [16], in which one aims to infer, from observational data, the counterfactual eﬀect on some measurement by changing the policy (more often called“intervention”in the statistics literature).”
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data. (2008, methodology): “Reviews of causal inference from this perspective are given by Holland (1986), Winship and Sobel (2004), Gelman and Meng (2004) and Rubin (2005).”
Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data (2007, methodology): “Reviews of causal inference from this perspective are given by Holland (1986), Winship and Sobel (2004), Gelman and Meng (2004) and Rubin (2005).”
Towards a critical race methodology in algorithmic fairness (2019, neutral): “There has also been signiﬁcant debate about the appropriate use of racial variables in studies aimed at identifying causal effects [46, 54, 55, 58, 67].”
A Distributional Approach for Causal Inference Using Propensity Scores (2006, background): “This inherent fact of observational life is called the fundamental problem of causal inference (Holland 1986).”
EDA for HLM: Visualization when Probabilistic Inference Fails (2005, background): “See, for example, Holland (1986) and Brady and Seawright (2004) for discussion of the fundamental role of comparison in establishing causal relationships.”
Causal inference with misspecified exposure mappings: separating definitions and assumptions (2021, background): “The term B1(a, b) stems from what Holland (1986) describes as the fundamental problem of causal inference, namely that a unit cannot simultaneously be assign to two different treatments.”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “In contrast to descriptive or predictive tasks, causal inference aims to understand how intervening on one variable affects another variable (Holland, 1986; Pearl, 2000; Morgan and Winship, 2015; Imbens and Rubin, 2015; Hernán and Robins, 2020).”
Testing for Granger-causality in quantiles (2018, methodology): “By using temporal precedence relationships, the notion of Granger-causality di ers from the causal concepts based on controlled variation that are present in Haavelmo (1944), Holland (1986), Pearl (1988), Heckman and Vytlacil (2007), among others.”