Interventions and Causal Inference

By Frederick Eberhardt, Richard Scheines. 2007

325 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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

In Statistics and Probability

In Causal Inference & Discovery

Citations in the corpus

Differentiable Causal Discovery from Interventional Data (2020, background): “A specific case that is often considered is (stochastic) perfect interventions (or hard, structural) [10, 48, 26] where p j (xj |xπG j ) = p (k) j (xj) for all j ∈ Ik, thus removing the dependencies with their parents (see Figure 1).”
Causal Discovery from Heterogeneous/Nonstationary Data (2019, background): “, 1995)) searches for the equivalence class which gives the highest score under some scoring criteria, such as the Bayesian Information Criterion (BIC), the posterior of the graph given the data (Heckerman et al., 1997), and the generalized score functions (Huang et al.”
Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “1, and note that the assigned values can be random, also), or soft interventions (Eberhardt and Scheines, 2007) (see also Sections 4.”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “Different types of “imperfect” interventions can be considered as well, see e.g., Eberhardt and Scheines (2007); Eaton and Murphy (2007); Mooij and Heskes (2013).”
Anchor regression: Heterogeneous data meet causality (2018, background): “Shift interventions can be seen as a special case of a “parametric”, “imperfect” or “dependent” intervention or a “mechanism change” [Eberhardt and Scheines, 2007, Korb et al., 2004, Tian and Pearl, 2001].”
Incremental causal effects (2019, methodology): “1 Related work For discrete treatments, Kennedy (2018) defines incremental propensity score interventions by multiplying the odds of receiving treatment. The author shows that the overlap assumption is not necessary to identify incremental propensity score interventions and develops an efficiency th”
Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory (2012, unknown): “Sobel, Tenenbaum, and Gopnik (2004) found that preschool children also would make correct causal inferences from more complex statistical patterns, particularly backward blocking. Backward blocking is a kind of causal inference that requires children to learn about the causal efficacy of an object u”
Agent Incentives: A Causal Perspective (2021, background): “More generally, a soft intervention on a variable X in an SCMM replaces f with a function g : dom(Pa ∪ {E})→ dom(X) (Eberhardt and Scheines 2007; Tian and Pearl 2001).”
Invariant Causal Prediction for Block MDPs (2020, background): “Each environment e ∈ E corresponds to a do-(Pearl, 2009) or soft (Eberhardt and Scheines, 2007) intervention on a single variable x i in the observation space.”
Datamodels: Predicting Predictions from Training Data (2022, background): “In particular, a long line of literature in causal inference focuses on intervention design (Eberhardt & Scheines, 2007); drawing upon this line of work may lead to a better choice of subsampling distribution.”
Causal Confusion in Imitation Learning (2019, methodology): “We operate in the interventional regime [55, 11, 49, 48] where a user may “experiment” to discover causal structures by assigning values to some subset of the variables of interest and observing the effects on the rest of the system.”
Algorithmic Recourse: from Counterfactual Explanations to Interventions (2020, background): “Such confounded/correlated interventions, referred to as fat-hand / non-atomic interventions (Eberhardt & Scheines, 2007), will be explored further in follow-up work, by modelling the world at different causally consistent levels (Rubenstein et al., 2017; Beckers & Halpern, 2019).”
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures (2021, background): “While Structural Causal Models (SCMs) provide a generative model over the data, they are hard to learn from data due to the non-identiﬁability of the causal models without interventional data [7] and the combinatorial nature of the space of DAGs [15].”
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, background): “Parametric interventions also preserve any correlations deriving from unobserved/hidden confounders [49].”
Learning linear cyclic causal models with latent variables (2012, background): “Eberhardt et al., 2005; Meganck et al., 2005; Nyberg and Korb, 2006; Eberhardt and Scheines, 2007; Eaton and Murphy, 2007).”
UvA-DARE ( Digital Academic Repository ) Causal Discovery with Continuous Additive Noise Models (2014, unknown): “For more details on soft interventions, see Eberhardt and Scheines (2007). Note that in general: p(x1, .”
Understanding Agent Incentives using Causal Influence Diagrams. Part I: Single Action Settings (2019, background): “n incentive (Deﬁnition 8). Instead of considering observing an extra node, we consider controlling an extra node, where control is formalized with soft interventions: Deﬁnition 12 (Soft intervention; Eberhardt and Scheines, 2007; Pearl, 2009, p. 74). A (soft) intervention cX on a non-decision node X”
Causal Network Learning from Multiple Interventions of Unknown Manipulated Targets (2016, background): “A causal graph G is causally sufficient if no latent vertices affect two or more vertices contained in G (Eberhardt and Scheines, 2007; Pearl, 2000).”