Epsilon-Identifiability of Causal Quantities

• URL: http://arxiv.org/abs/2301.12022v1
• Date: Fri, 27 Jan 2023 23:16:57 GMT
• Title: Epsilon-Identifiability of Causal Quantities
• Authors: Ang Li, Scott Mueller, Judea Pearl
• Abstract summary: We show how partial identifiability is still possible for several probabilities of causation. In particular, we show how unidentifiable causal effects and counterfactual probabilities can be narrowly bounded.
• Score: 17.565045120151865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from the data sources available. This paper shows how partial identifiability is still possible for several probabilities of causation. We term this epsilon-identifiability and demonstrate its usefulness in cases where the behavior of certain subpopulations can be restricted to within some narrow bounds. In particular, we show how unidentifiable causal effects and counterfactual probabilities can be narrowly bounded when such allowances are made. Often those allowances are easily measured and reasonably assumed. Finally, epsilon-identifiability is applied to the unit selection problem.

Related papers

• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Identifiable Latent Polynomial Causal Models Through the Lens of Change [82.14087963690561]
  Causal representation learning aims to unveil latent high-level causal representations from observed low-level data. One of its primary tasks is to provide reliable assurance of identifying these latent causal models, known as identifiability.
  arXiv  Detail & Related papers  (2023-10-24T07:46:10Z)
• Partial Identification of Causal Effects Using Proxy Variables [19.23377338970307]
  Proximal causal inference is a recently proposed framework for evaluating causal effects in the presence of unmeasured confounding. In this paper, we propose partial identification methods that do not require completeness and obviate the need for identification of a bridge function.
  arXiv  Detail & Related papers  (2023-04-10T04:18:27Z)
• Bounding probabilities of causation through the causal marginal problem [12.542533707005092]
  Probabilities of causation play a fundamental role in decision making in law, health care and public policy. In many clinical trials and public policy evaluation cases, there exist independent datasets that examine the effect of a different treatment each on the same outcome variable. Here, we outline how to significantly tighten existing bounds on the probabilities of causation, by imposing counterfactual consistency between SCMs constructed from such independent datasets.
  arXiv  Detail & Related papers  (2023-04-04T12:16:38Z)
• Interventional Causal Representation Learning [75.18055152115586]
  Causal representation learning seeks to extract high-level latent factors from low-level sensory data. Can interventional data facilitate causal representation learning? We show that interventional data often carries geometric signatures of the latent factors' support.
  arXiv  Detail & Related papers  (2022-09-24T04:59:03Z)
• Identifying Weight-Variant Latent Causal Models [82.14087963690561]
  We find that transitivity acts as a key role in impeding the identifiability of latent causal representations. Under some mild assumptions, we can show that the latent causal representations can be identified up to trivial permutation and scaling. We propose a novel method, termed Structural caUsAl Variational autoEncoder, which directly learns latent causal representations and causal relationships among them.
  arXiv  Detail & Related papers  (2022-08-30T11:12:59Z)
• Bounding Counterfactuals under Selection Bias [60.55840896782637]
  We propose a first algorithm to address both identifiable and unidentifiable queries. We prove that, in spite of the missingness induced by the selection bias, the likelihood of the available data is unimodal.
  arXiv  Detail & Related papers  (2022-07-26T10:33:10Z)
• Identifiability of Sparse Causal Effects using Instrumental Variables [11.97552507834888]
  In this paper, we consider linear models in which the causal effect from covariables $X$ on a response $Y$ is sparse. We provide conditions under which the causal coefficient becomes identifiable from the observed distribution. As an estimator, we propose spaceIV and prove that it consistently estimates the causal effect if the model is identifiable.
  arXiv  Detail & Related papers  (2022-03-17T15:15:52Z)
• Typing assumptions improve identification in causal discovery [123.06886784834471]
  Causal discovery from observational data is a challenging task to which an exact solution cannot always be identified. We propose a new set of assumptions that constrain possible causal relationships based on the nature of the variables.
  arXiv  Detail & Related papers  (2021-07-22T14:23:08Z)
• Causes of Effects: Learning individual responses from population data [23.593582720307207]
  We study the problem of individualization and its applications in medicine. For example, the probability of benefiting from a treatment concerns an individual having a favorable outcome if treated and an unfavorable outcome if untreated. We analyze and expand on existing research by applying bounds to the probability of necessity and sufficiency (PNS) along with graphical criteria and practical applications.
  arXiv  Detail & Related papers  (2021-04-28T12:38:11Z)
• Identifying Causal Effects via Context-specific Independence Relations [9.51801023527378]
  Causal effect identification considers whether an interventional probability distribution can be uniquely determined from a passively observed distribution. We show that deciding causal effect non-identifiability is NP-hard in the presence of context-specific independence relations. Motivated by this, we design a calculus and an automated search procedure for identifying causal effects in the presence of CSIs.
  arXiv  Detail & Related papers  (2020-09-21T11:38:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.