Signed directed acyclic graphs for causal inference
By Tyler J. VanderWeele, James M. Robins. 2010
97 citations recorded by the source corpus.
Summary. Formal rules governing signed edges on causal directed acyclic graphs are described and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an intervention in the presence of intermediate variables. The incorporation of signed edges in the directed acyclic graph causal framework furthermore allows for the development of rules governing the relationship between monotonic effects and the sign of the covariance between two variables. It is shown that when certain assumptions about monotonic effects can be made then these results can be used to draw conclusions about the presence of causal effects even when data are missing on confounding variables.
Subfields: Artificial Intelligence, Statistics and Probability
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
- Peter Buhlmann
- Nicolai Meinshausen
- Jan Peters
- Bernhard Schölkopf
- Marloes H. Maathuis
- Dominik Rothenhausler
- Bin Yu
- Yoshua Bengio
In Statistics and Probability
- Peter Buhlmann
- Jan Peters
- Nicolai Meinshausen
- Bin Yu
- Sara A. van de Geer
- Dominik Rothenhausler
- Bernhard Schölkopf
- Cun-Hui Zhang
Citations in the corpus
- Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “…interventional data [Spirtes et al., 2000, Chickering, 2002, Castelo and Kocka, 2003, Kalisch and B¨uhlmann, 2007, He and Geng., 2008, Hauser and B¨uhlmann, 2015, cf.], and finally infer the iden-tifiable causal effects or provide some bounds [Maathuis et al., 2009, VanderWeele and Robins, 2010, cf.].”
- Causal Dantzig: Fast inference in linear structural equation models with hidden variables under additive interventions (2017, background): “Based on the Markov equivalence class, some causal effects and often only bounds for them can be inferred, see for example Maathuis et al. [2009] and VanderWeele and Robins [2010].”
- Minimal sufficient causation and directed acyclic graphs (2009, background): “Rules for the propagation of signs have been developed elsewhere [38, 39, 41] and, as seen from Proposition 2, are useful for determining the sign of covariances; however, as will be seen below, rules for deriving the sign of conditional”