Identifiability of Path-Specific Effects ∗
By Chen Avin, Ilya Shpitser. 2005
Subfields: 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 Statistics and Probability
- Bernhard Schölkopf
- Tyler J. VanderWeele
- Judea Pearl
- Ilya Shpitser
- Dominik Janzing
- Xintao Wu
- Lu Zhang
- Yongkai Wu
In Causal Inference & Discovery
- Bernhard Schölkopf
- Xintao Wu
- Lu Zhang
- Dominik Janzing
- Yongkai Wu
- Silvia Chiappa
- Judea Pearl
- Ilya Shpitser
Citations in the corpus
- Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias (2020, background): “The causality literature often focuses on assumptions needed for identification of mediation effects from observed data (Pearl, 2001; Avin et al., 2005; Imai et al., 2010b).”
- PC-Fairness: A Unified Framework for Measuring Causality-based Fairness (2019, methodology): “According to [20], for the variables of X that lie on both π and ¯ π , referred to as witness variables/nodes [1], we need to consider two sets of values, one obtained by treating them on π and the other obtained by treating them on ¯ π .”
- Mediation Analysis: A Practitioner's Guide. (2016, background): “It can be shown (8) that if Figure 3 represents the correct causal structure ov i it y A cce B = γλ γ + λ − . can alter the direct and indirect effects) we can take our direct and indirect effects estimates and their confidence intervals from the observed data and divide the direct effect estimate an”
- Complete Identification Methods for the Causal Hierarchy (2008, background): “This simple generalization of the twin network model was considered by Avin et al. (2005), and was called there the parallel worlds graph.”
- The Mediation Formula: A Guide to the Assessment of Causal Pathways in Nonlinear Models (2011, background): “Such applications motivate the analysis of path-specific effects, that is, the effect of X on Y through a selected set of paths (Avin et al., 2005).”
- Investigating Gender Bias in Language Models Using Causal Mediation Analysis (2020, background): “A promising direction is to focus on path-specific effects (Avin et al., 2005), to identify the exact mechanisms through which biases arise.”
- Quantifying causal influences (2012, methodology): “Mediation analysis : [2, 15, 19] explore how to separate the influence of X on Y into parts that can be attributed to specific paths by “blocking” other paths.”