Causal inference tries to quantify the effect of a change in $X$ on $Y$ whilst holding constant or eliminating all other relevant factors which might influence this relationship.

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Causal inference from Cross sectional study

As far I know that, causal inference can make for longitudinal study. Is their anyway way to make causal inference from cross sectional study design? If yes, how can I do this? Please share if any ...
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Dealing with constraints in probabilistic graphical models

Suppose I have a graphical model with i.i.d. $\Lambda_i \sim exp(\lambda),\ \ i = 1,...,n$, and $\bf{\Lambda} = \sum_{i=1}^{n}\Lambda_i$. Imagine that that these $n$ lambda and this capital ...
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How to identify the underlying mechnism in regression (to preserve the causality)?

everyone,I recently encounter a problem when doing some empirical work. Say, with a cross section dataset,I'm studying the effect from A to Y, including a branch of control variables X,of which A is a ...
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28 views

How to do regression if one is not sure of dependent variable?

I have data on age, gender, height and weight of subjects, and also levels of 2 chemicals in blood of these subjects (chem1 and chem2). It is not clear whether chem1 affects chem2 or chem2 affects ...
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Isolating influence of sampling from actual change

Say I want to evaluate teams' batting coaches in a hypothetical baseball league. It's an unusual league in that there is no control over (and large fluctuation within) the number of at-bats each ...
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146 views

What are the main differences between Granger's and Pearl's causality frameworks?

Recently, I ran across several papers and online resources that mention Granger causality. Brief browsing through the corresponding Wikipedia article left me with the impression that this term refers ...
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37 views

Should I use the t-test or the Wilcoxon rank sum test given these qqplots and Shapiro-Wilk stats [duplicate]

I have 4 groups and I want to test if the pairwise difference in means are significantly different. There are 6 pairwise differences. The QQnorm plots of the 4 groups look like this: The ...
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22 views

Establishing causality under conditions of certainty

I'm currently reading "Causality: Models, Reasoning, and Inference" by Judea Pearl. Early on, he states that the development assumes that there are no certain entailments, no 1 or 0 probabilities -- ...
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30 views

Names for some canonical directed causal graphs/illustrations of some canonical causal relationships?

Certain names are used for structures or node relationships that appear in acyclic, directed graphs (DAGs). Often these DAGs are interpreted causally. Here's a partial list for relationships that ...
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What are the best empirical studies comparing causal inference with experimental, quasi-experimental, and non-experimental techniques?

The Issue: People attempt to draw causal inferences using many different statistical techniques (e.g. regression, propensity score matching, regression discontinuity, instrumental variables, etc.). ...
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Necessary conditions for causality

E. Tufte writes: 'Probably the shortest true statement that can be made about causality and correlation is "Empirically observed covariation is a necessary but not sufficient condition for ...
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Need a statistic for comparing “strength” of Markov blankets in a Bayesian network

Working with Bayesian networks. I take a given network structure and fit its parameters on data. I am looking for a statistic based on those parameter estimates that allows me to compare Markov ...
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39 views

Time domain regression - determining lagged predictors

Determining lifestyle factors affecting a medical symptom I have a dataset with n=200 records corresponding to contiguous days and consisting of 1 continuous output variable (a medical symptom) and ...
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174 views

Does rejection of null hypothesis in multiple regression entail causation?

We make a model of the following form: $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \epsilon $$ and with $n=1,000$, $\hat\beta_1$ has a p-value <0.001. If our data and data collection meets ...
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87 views

Can two linear regression variables be perfectly correlated but not share a single causal chain ancestor?

A causal chain lists event (or fact) $y$ with all its causal antecedents. We make a model of the following form: $$ y = \beta_0 + \beta_1x_1 + \beta_2x_2 + \epsilon $$ $\hat\beta_1$ has a p-value ...
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Measuring effects with longitudinal data

Problem: I have sales data through time e.g. how much each user spent on each shopping trip. I am interested in certain events (think users switching to Amazon Prime for instance). I know the date ...
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Mahalanobis Distance Matching

When doing Mahalanobis matching to improve covariate balance for causual inference, how come if the treated units aren't subsampled only the control covariance matrix is used $S_c$ as opposed to the ...
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107 views

How does one verify causation?

After we have shown that two quantities are correlated how do we infer that the relationship is causal? And furthermore which one causes what? Now in theory one can use a "random assignment" (whatever ...
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67 views

Under what conditions does correlation imply proximity to causation?

I've recently had an experience with the whole "correlation does not imply causation", which is certainly true as far as a true/false proposition is concerned, but which also seems to be used too ...
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107 views

Clarification on ARDL/Unrestricted Error Correction Model

I have a few questions about unrestricted error correction models. The UECM for a model where $Y$ is the dependent variable and $x$ is the sole independent variable is given by, $$ \Delta ...
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508 views

How fair is it to use the word “predict” for (logistic) regression?

My understanding is that even regression does not give causality. It can only give association between y variable and x variables and possibly a direction. Am I correct? I've often found phrases ...
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74 views

Self Selection bias for estimating a valiable correlated to the selector

I am trying to find a way to see if a measured variable between two groups is significantly different. This would normally be done through a t-test if the two groups were randomly selected from the ...
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255 views

Confidence interval for average treatment effect from propensity score weighting?

I am trying to estimate the average treatment effect from observational data using propensity score weighting (specifically IPTW). I think I am calculating the ATE correctly, but I don't know how to ...
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normalizing predictor by another predictor

I'm fitting a linear model with outcome $Y$. I have measurements for variables $X_1$ and $X_2$. I hypothesize that $X_1$ and $Y$ are linearly related. I want to know the slope and significance of ...
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$3^3$ factorial design

Suppose in a $3^3$ factorial design, factor A has three levels. We want to test the significance of A and after setting hypothesis $$H_0:\alpha_i=0 \quad\text{for}\quad i=1,2,3 \quad\text{Vs.}\quad ...
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Is it okay to run CausalImpact in R on successive portions of a time series?

We’re doing some advertising tests with test and control groups very similar to the example in the Google Research Causal Impact publication except we’re doing state tests and not DMA. I just have a ...
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Propensity Score can be used as a covariate in regression?

I have treated and control groups with a problem of selection in the treatment group. I am interested in the identification of the following model: $y= exp(X^\prime\beta + \alpha\cdot T)$ where $T$ ...
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Multilevel model for causal inference using observational data

I am trying to follow the lecture notes by Imbens/Wooldridge (http://www.nber.org/WNE/lect_10_diffindiffs.pdf) on difference-in-differences estimation. In page 4, they discuss the general framework ...
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132 views

group fixed-effects, not individual-fixed effects using plm in R

I am analyzing some data to evaluate the impact (causal effect) of a program that is delivered at group level (a village). The outcome of interest is measured at the individual level (individuals ...
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141 views

Transfer function in forecasting models - interpretation

I am occupied with ARIMA modelling augmented with exogenous variables for promotional modelling purposes and i have hard time explaining it to business users. In some cases software packages end up ...
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36 views

Explaining why the slope varies in varying slope model?

When I fit a multilevel varying slope model, it is easy to summarize the variation in slope. However, I have not yet seen any materials that discusses how to explain such variation (i.e. what about ...
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138 views

Is there evidence of mediation? Need help with interpretation of mediation analysis results

I have performed a mediation analysis. I have an independent variable T, a mediator M, and outcome Y. (All 3 variables are binary, and I use logit.) (While I used Stata ...
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97 views

Is there any correlation or causation here?

I have the following data, where 2 properties (P1 and P2) can be either True or False ...
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32 views

How to find three probabilities with two different values or ratings?

I would like to know how to find three probabilities of two values.... Specifically...I want to know the three soccer venues (HOME DRAW AWAY) proabilities with two ratings... Example: I have two ...
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From Identification to Estimation

I'm currently reading Pearl's piece (Pearl, 2009, 2nd edition) on causality and struggle to establish the link between nonparametric identification of a model and actual estimation. Unfortunately, ...
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140 views

Would a simple OLS regression work with this example?

First and foremost I would greatly appreciate any help you can provide me with. I am writing my undergraduate thesis on the rise of populism in France. The relationship I am trying to better ...
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248 views

Does adjusting for superfluous variables bias OLS estimates?

The usual textbook treatment of adjusting for superfluous variables in OLS states that the estimator is still unbiased, but may have larger variance (see, for example, Greene, Econometric Analysis, ...
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How to program automated shrinkage for a subset of terms in R?

I've got data from a randomized experiment that includes a lot of covariates. I'm interested $\delta$ from a model of the form $y = g(\delta T + X'\beta+ \epsilon)$, where $T$ is randomly assigned and ...
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42 views

Confusion about features selection for inference analysis with lm/glm

I need a bit of tutoring about grasping the true meaning of linear regression analysis. I'd like some help in understanding well the relationships between predictors and and the meaning of adding and ...
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235 views

Propensity Score Analysis with continuous treatment

I have an observational dataset of about two dozen observed variables (continuous or discrete), plus a continuous variable of which I would like to measure the causal impact of on my dependent ...
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Adjusting for a mediator to capture cross sectional relationship

We are fitting a linear model using cross-sectional data to inspect the relationship between some exposure and an outcome (disease status, measured continuously). Duration of disease was also captured ...
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Meta-analysis: Talking about power and inference (to power)

I need a little help or reassurance concerning how to explain power "to power", i.e. to decision makers that are not well versed in statistics. The problem is this: I have done three empirical ...
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Detecting parameter influence

I have a data set consisting of a system's responses to various test configurations. Every test configuration corresponds to a different parameter set. These parameters can have either continuous ...
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58 views

Identifying What Causes a Variable to Increase

Say I have a dataset with several continuous and categorical variables, and I want to identify what variables (values or properties of these variables) may cause one of the continuous variables to ...
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32 views

How can we combine learnings from multiple experiments in a single causal model?

I would like to use a causal network modelling to model the interaction of several variables and the effects of interventions. I have measurements for all priors of the model, that is without any ...
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Do you need causal models when doing counterfactual predictions?

I am modeling the impact the number of a certain type of company (bottom of pyramid (BOP) companies, ie. companies that cater to the poorest consumers) have on market price. I considered the ...
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48 views

Is this mediation or a simple path?

This is my model adapted from a study. I want to know whether I can only study it as a path analysis without studying mediation effect (1 $\longrightarrow$ 5 direct effect, as well as indirect ...
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29 views

Inference from a sometimes-random time-series

Let's say we have two cointegrated time-series, $Y_{1}$ and $Y_{2}$, and I want to assess the causal impact of $Y_{1}$ on $Y_{2}$. There is good reason to think that both variables are influenced by a ...
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Choice of dependent variable: Differencing or Controlling?

I was running some analysis where I suspect that a treatment $D$, has opposite effects on two variables $Y^A$ and $Y^B$. To show that, I was thinking about two strategies: 1. Differencing Running ...
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Dropped cases from matched studies

We have cohort data and a rare exposure which we are matching to controls in a large epidemiologic dataset. The matching variable is a deidentified neighborhood indicator (cluster) which guarantees ...