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ACM Transactions on Intelligent Systems and Technology (TIST) - Special Issue on Causal Discovery and Inference, Volume 7 Issue 2, January 2016

On Estimation of Functional Causal Models: General Results and Application to the Post-Nonlinear Causal Model
Kun Zhang, Zhikun Wang, Jiji Zhang, Bernhard Schölkopf
Article No.: 13
DOI: 10.1145/2700476

Compared to constraint-based causal discovery, causal discovery based on functional causal models is able to identify the whole causal model under appropriate assumptions [Shimizu et al. 2006; Hoyer et al. 2009; Zhang and Hyvärinen 2009b]....

From Observational Studies to Causal Rule Mining
Jiuyong Li, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, Bingyu Sun, Saisai Ma
Article No.: 14
DOI: 10.1145/2746410

Randomised controlled trials (RCTs) are the most effective approach to causal discovery, but in many circumstances it is impossible to conduct RCTs. Therefore, observational studies based on passively observed data are widely accepted as an...

Gestures à Go Go: Authoring Synthetic Human-Like Stroke Gestures Using the Kinematic Theory of Rapid Movements
Luis A. Leiva, Daniel Martín-Albo, Réjean Plamondon
Article No.: 15
DOI: 10.1145/2799648

Training a high-quality gesture recognizer requires providing a large number of examples to enable good performance on unseen, future data. However, recruiting participants, data collection, and labeling, etc., necessary for achieving this goal...

Event Extraction using Structured Learning and Rich Domain Knowledge: Application across Domains and Data Sources
Einat Minkov
Article No.: 16
DOI: 10.1145/2801131

We consider the task of record extraction from text documents, where the goal is to automatically populate the fields of target relations, such as scientific seminars or corporate acquisition events. There are various inferences involved in the...

Section: On Estimation of Functional Causal Models

Preface to the ACM TIST Special Issue on Causal Discovery and Inference
Kun Zhang, Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, Judea Pearl
Article No.: 17
DOI: 10.1145/2840720

Sharp Bounds on Survivor Average Causal Effects When the Outcome Is Binary and Truncated by Death
Na Shan, Xiaogang Dong, Pingfeng Xu, Jianhua Guo
Article No.: 18
DOI: 10.1145/2700498

In randomized trials with follow-up, outcomes may be undefined for individuals who die before the follow-up is complete. In such settings, Frangakis and Rubin [2002] proposed the “principal stratum effect” or “Survivor Average...

Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes
Hua Chen, Peng Ding, Zhi Geng, Xiao-Hua Zhou
Article No.: 19
DOI: 10.1145/2668135

Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary...

Bounds on Direct and Indirect Effects of Treatment on a Continuous Endpoint
Peng Luo, Zhi Geng
Article No.: 20
DOI: 10.1145/2668134

Direct effect of a treatment variable on an endpoint variable and indirect effect through a mediate variable are important concepts for understanding a causal mechanism. However, the randomized assignment of treatment is not sufficient for...

Causal Discovery on Discrete Data with Extensions to Mixture Model
Furui Liu, Laiwan Chan
Article No.: 21
DOI: 10.1145/2700477

In this article, we deal with the causal discovery problem on discrete data. First, we present a causal discovery method for traditional additive noise models that identifies the causal direction by analyzing the supports of the conditional...

Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference
Seth R. Flaxman, Daniel B. Neill, Alexander J. Smola
Article No.: 22
DOI: 10.1145/2806892

In applied fields, practitioners hoping to apply causal structure learning or causal orientation algorithms face an important question: which independence test is appropriate for my data? In the case of real-valued iid data, linear dependencies,...

Learning Perceptual Causality from Video
Amy Fire, Song-Chun Zhu
Article No.: 23
DOI: 10.1145/2809782

Perceptual causality is the perception of causal relationships from observation. Humans, even as infants, form such models from observation of the world around them [Saxe and Carey 2006]. For a deeper understanding, the computer must make similar...

A Novel Continuous and Structural VAR Modeling Approach and Its Application to Reactor Noise Analysis
Marina Demeshko, Takashi Washio, Yoshinobu Kawahara, Yuriy Pepyolyshev
Article No.: 24
DOI: 10.1145/2710025

A vector autoregressive model in discrete time domain (DVAR) is often used to analyze continuous time, multivariate, linear Markov systems through their observed time series data sampled at discrete timesteps. Based on previous studies, the DVAR...

A Causal Approach to the Study of TCP Performance
Hadrien Hours, Ernst Biersack, Patrick Loiseau
Article No.: 25
DOI: 10.1145/2770878

Communication networks are complex systems whose operation relies on a large number of components that work together to provide services to end users. As the quality of these services depends on different parameters, understanding how each of them...