Modeling confounding by half-sibling regression

We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as “half-sibling regression,” is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both independent and identically distributed as well as time series data, respectively, and illustrate the potential of the method in a challenging astronomy application.

Download Full-text

Causal inference with multiple time series: principles and problems

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2011.0613 ◽

2013 ◽

Vol 371 (1997) ◽

pp. 20110613 ◽

Cited By ~ 42

Author(s):

Michael Eichler

Keyword(s):

Time Series ◽

Causal Inference ◽

Granger Causality ◽

Latent Variables ◽

Time Series Data ◽

Series Data ◽

Multiple Time ◽

Identification Algorithm ◽

Multiple Time Series ◽

Theoretical Justification

I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.

Download Full-text

Causal inference from noisy time-series data — Testing the Convergent Cross-Mapping algorithm in the presence of noise and external influence

Future Generation Computer Systems ◽

10.1016/j.future.2016.12.009 ◽

2017 ◽

Vol 73 ◽

pp. 52-62 ◽

Cited By ~ 16

Author(s):

Dan Mønster ◽

Riccardo Fusaroli ◽

Kristian Tylén ◽

Andreas Roepstorff ◽

Jacob F. Sherson

Keyword(s):

Time Series ◽

Causal Inference ◽

Time Series Data ◽

Series Data ◽

External Influence ◽

Mapping Algorithm ◽

Noisy Time Series ◽

Convergent Cross Mapping

Download Full-text

Future Prediction of Regional City based on Causal Inference using Time-series Data

Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications ◽

10.5220/0005961902030210 ◽

2016 ◽

Author(s):

Katsuhito Nakazawa ◽

Tetsuyoshi Shiota ◽

Tsutomu Tanaka

Keyword(s):

Time Series ◽

Causal Inference ◽

Time Series Data ◽

Series Data ◽

Future Prediction

Download Full-text

Future Prediction of Regional City Using Causal Inference Based on Time Series Data

Advances in Intelligent Systems and Computing - Simulation and Modeling Methodologies, Technologies and Applications ◽

10.1007/978-3-319-69832-8_16 ◽

2017 ◽

pp. 280-296

Author(s):

Katsuhito Nakazawa ◽

Tetsuyoshi Shiota ◽

Tsutomu Tanaka

Keyword(s):

Time Series ◽

Causal Inference ◽

Time Series Data ◽

Series Data ◽

Future Prediction

Download Full-text

Applying a propensity score-based weighting model to interrupted time series data: improving causal inference in programme evaluation

Journal of Evaluation in Clinical Practice ◽

10.1111/j.1365-2753.2010.01504.x ◽

2010 ◽

Vol 17 (6) ◽

pp. 1231-1238 ◽

Cited By ~ 73

Author(s):

Ariel Linden ◽

John L. Adams

Keyword(s):

Time Series ◽

Propensity Score ◽

Causal Inference ◽

Time Series Data ◽

Interrupted Time Series ◽

Programme Evaluation ◽

Series Data

Download Full-text

A SIMPLE TEST FOR HIDDEN PERIODICITY IN TIME SERIES DATA

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127495000211 ◽

1995 ◽

Vol 05 (01) ◽

pp. 265-269 ◽

Cited By ~ 1

Author(s):

MICHAEL ROSENBLUM ◽

JÜRGEN KURTHS

Keyword(s):

Experimental Data ◽

Time Series ◽

Spectral Analysis ◽

Additive Noise ◽

Time Series Data ◽

Series Data ◽

Simple Test ◽

Data Set ◽

Dynamical Characteristics

We would like to draw the attention of specialists in time series analysis to a simple but efficient algorithm for the determination of hidden periodic regimes in complex time series. The algorithm is stable towards additive noise and allows one to detect periodicity even if the examined data set contains only a few periods. In such cases it is more suitable than other techniques, such as spectral analysis or recurrence map. We recommend the use of this test prior to the evaluation of attractor dimensions and other dynamical characteristics from experimental data.

Download Full-text

Causal Inference for fMRI Time Series Data With Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat

Journal of the American Statistical Association ◽

10.1080/01621459.2014.922886 ◽

2014 ◽

Vol 109 (507) ◽

pp. 967-976 ◽

Cited By ~ 6

Author(s):

Michael E. Sobel ◽

Martin A. Lindquist

Keyword(s):

Time Series ◽

Causal Inference ◽

Time Series Data ◽

Systematic Errors ◽

Series Data ◽

Social Evaluative Threat ◽

Fmri Time Series

Download Full-text

Separation of random noise models from seasonal time series data

Applied Mathematical Modelling ◽

10.1016/0307-904x(77)90045-2 ◽

1977 ◽

Vol 1 (7) ◽

pp. 367-371

Author(s):

P.B. Reddy

Keyword(s):

Time Series ◽

Time Series Data ◽

Random Noise ◽

Series Data ◽

Seasonal Time Series ◽

Noise Models

Download Full-text

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

Download Full-text

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

Download Full-text