The relationship between cause and effect.

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Matching data before regression (multiple treatment variables)

I have the dataset for the health of patients along with various treatments they were given. In a normal case, I would just use linear regression to fit a model [y ~ t1 + t2 + t3 ... +tn]. This will ...
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8 views

Interpretation of CausalImpact package output

I have a question on how to interpret the predictors used in building the model. As per this link https://google.github.io/CausalImpact/CausalImpact.html I used the following code ...
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13 views

Control group construction in Synthetic Control Method

I want to use the Synthetic Control Method to estimate the effect of the adaption of a new ballot institution (treatment) on fiscal policy. My sample consists of 20 units observed at 100 time points. ...
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7 views

How to deal with heterogeneous data set

Problem I have a dataset that contains three types of objects: simple objects, object groups and meta-groups. Meta-groups contain simple objects and groups. Each simple object correspond to an ...
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8 views

Granger Causality Test on differenced data

I was just wondering that if you had to induce stationarity on the data set.. so therefore both variables are now I(1).. when checking that causality of them would you now use the differenced data or ...
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5 views

Controlling for a variable highly correlated with the variable of interest

I want to see if there's a relationship between $x$ and $y$. A variable $z$ is highly (but not perfectly) correlated with $x$. I want to check that $z$ is only related $y$ through $x$, and not ...
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3answers
46 views

What is the difference between correlation, causation and prediction?

Suppose we have a set of events $\Omega$, containing events $A$ and $B$. My econometrics professor tried to distinguish the following three terms today. Causation --- $A$ causes $B$ if the ...
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1answer
19 views

Causal and conditional probability

I was trying to understand causal probabilities better by reading this article http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ I was interested in the last example where we have 3 ...
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4answers
104 views

Why does propensity score matching work for causal inference?

Propensity score matching is used for make causal inferences in observational studies (see the Rosenbaum / Rubin paper). What's the simple intuition behind why it works? In other words, why if we ...
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5 views

CausalImpact and choosing the start of effect time-frame

Is it probable, to experimentally choose a prior starting point to the factual starting point of a n effect in order to validate the package's results? I guess the point gets more clear if you look ...
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19 views

How does Causalimpact work? (please see more specific questions in the description)

How does CausalImpact behave when the number of data points in the time-series is unequal to n times the set length of a season (for example when there are 30 data points with the length of the ...
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14 views

Extracting influence counts from Model variables or data

To idetifying the important activity performed from users who have been converted in last N days. So, I have tried GLM, Rpart and Random forest models which can give me the impoprtant activities (in ...
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15 views

Precision adjustment in biomarker analyses

In a causal modeling framework, we are concerned with measuring an association between an exposure and an outcome. To do that, we usually fit a regression model for the outcome as the "y" on the ...
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146 views

Coalitional effect in logistic regression and assessing explanarory variable contribution

I have a problem that could be described as logistic regression with all dichotomous variables: 1 response variable (DV) Y (I would call it later as a feature/violet star) and 5 explanatory variables ...
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0answers
35 views

Should I use such adjustment

In this question I would like to ask you to choose between two simple scenarios of testing differences of rates between two random variable of Poisson distribution over different time periods. We ...
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0answers
40 views

Granger causality test vs my own custom causality test

I have a panel data regression with fixed effects, but for simplicity it is such that a lagged explanatory variable is significantly correlated with the dependent variable, which I believe is ...
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0answers
28 views

Marketing/Sales Mix/Response Models: approaches and comparisons

CV/SO Community: I am probably skirting (or crossing) the line of the preference for questions that can be answered vs. those that can (only) be discussed. That said, I'm trying to wrap my head ...
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18 views

Multiple intervention causal inference

I am currently trying to implement a causal inference in my graph (DAG). What I have is the structure of the graph and 3 groups of multiple intervention data. So, for simple example : The structure ...
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0answers
7 views

Coefficient of Quasi-Randomized Control Trial with different Treatment Intensity

I need some thoughts on the following problem which I have not been able to solve. I have thought of using different methods including OLS, FE, and PSM but I am not sure what to use. Here is the ...
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0answers
15 views

Can external cause ever be discounted?

We are often advised not to confuse correlation with causation. Fortunately techniques do exist to assess the likelihood that an outcome does indeed result from a cause. 1 2 But in some cases it is ...
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1answer
31 views

causal impact - adding multiple control groups

I want to run an analysis using causal impact tool. I have one test group but multiple control groups. Can I use multiple control groups all together in one model? Eg: Y = test and A,B,C as control ...
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1answer
52 views

What does the error “pre.period must span at least 3 time points” in the CausalImpact R package mean?

I've been encountering the error "pre.period must span at least 3 time points" when using the package. Can someone help me understand why the package requires me to have at least 3 time points and ...
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1answer
91 views

What are the pros and cons of employing LASSO for causality analysis?

It looks like social sciences are impressed by Statistical Learning and its results. A couple of months ago, I heard Imbens saying: "LASSO is the new OLS". My problem with this is that I've been ...
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1answer
20 views

Figuring out causation from event data?

Suppose I have event data showing these things: users who signed up users who signed up and then went on to do main important activity users who signed up and then went on to invite a coworker ...
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17 views

When does the Rubin Causal Model fail in practice?

The Rubin Causal Model frames the causal inference question as the problem of inferring missing potential outcomes (what the outcome would have been if a unit had received a different treatment) in ...
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2answers
47 views

Identifying a confounder

I'm trying to check whether a variable is a confounder or not. Specifically, for a randomized trial where I want to investigate the effects of a reduction in class size on student performance, would ...
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2answers
173 views

Granger causality and non-linear regression

I’m new to Granger Causality concept. I know that the “Granger causality” is a statistical concept of causality that is based on prediction. According to Granger causality, if a time series X ...
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0answers
17 views

Treament By Covariate Interactions with Propensity Score Weighting

I am trying to estimate the causal effect of a treatment on an outcome using propensity score weighting. I estimated the propensity scores and verified covariate balance with a number of covariates, ...
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21 views

Causality between non stationary time series

I have two financial time series since both are non-stationary what is the best method to calculate the causality between two non-stationary time series?
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34 views

Adjusting for confounders when the investigated exposures are gene mutations

I'm delving into causality and directed acyclic graph for choosing the right covariate structure for multivariable regression analysis. Reading Pearl work, I understood that one should adjust only ...
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0answers
23 views

Real time Causality calculation for financial time series

I have 0.2 million financial time series data (1-minute data, each minute 1 sample data point), I want to find the causality in real time like if someone give me a data point how I know that one point ...
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1answer
54 views

Causality between multiple time series

I have 1000 financial time series (closing prices), I am using Toda-Yamamoto test. It is impossible to calculate the causality manually as there are $C_{1000}^{2-1000}$ cases. Is there any way in R ...
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17 views

Causal inference designs and research questions

Consider the following two research questions from Heckman, 2006: P.1 Forecasting the impacts (constructing counterfactual states) of interventions implemented in one environment in other ...
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1answer
41 views

What is the result of violated exclusion restrictions?

I have a question regarding exclusion restrictions in instrumental variable design. If I have an instrumental variable, which is also somewhat related to the outcome, would that (and how) cause ...
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0answers
17 views

No unmeasured confounders assumption

I am looking for a graphical interpretation of the no unmeasured confounders (NUC) assumption used in the case of dynamic treatment regime (Chakraborty and Moodie, 2013, p.13). I can't see how for any ...
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1answer
38 views

Is there sense in applying causal inference methods to variables with low correlation?

This question is somehow similar to Does causation imply correlation?, but what I would like to know is there any sense in applying a causal inference methods when we have a low correlation level. I'm ...
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1answer
47 views

Can fixed effects be “post-treament”?

I have located a natural experiment in a time series cross-sectional dataset, but I am unsure of whether or not to include unit-level fixed effects in my models. I have produced a toy example that ...
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24 views

Relationship of WTI oil with selected US sectors before, during and after a crisis

My group and I want to analyze the relationship of WTI oil with selected US sectors before, during and after a crisis. We use daily data from Datastream. Our time intervals are splitted in three ...
3
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1answer
95 views

Reverse causality, a bigger problem than I initially thought?

Take a standard regression framework: $$ Y_{it} =\beta X_{it} + \epsilon_{it}$$ Assume for simplicity that no omitted variables exist, nor are there simultaneity or measurement problems. In several ...
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26 views

Granger causality with matrices [duplicate]

I have a short question regarding Granger causality. I understand Granger only intuitively. If I am correct, it happens when $y_{t-1}$ and $x_t$ are better in explaining $y_t$ than only $y_{t-1}$. ...
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40 views

reverse causality when dependent variable and independent variable are observed on different levels

I am trying to regress the following: $$ Y_{ijt} = \gamma \cdot P_{jt} +X_{ijt} \beta+\epsilon_{ijt} $$ The j dimension is only indicated for clarity, the regression is a panel regression on i-t ...
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1answer
323 views

The difference between average and marginal treatment effect

I have been reading some papers, and I am unclear about the specific definitions of Average Treatment Effect (ATE), and Marginal Treatment Effect (MTE). Are they the same? According to Austin... ...
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48 views

Selecting Control Time Series for CausalImpact package in R

I'm currently using the CausalImpact package in R to analyze advertising impact on single store sales. I am working with daily sales data from nearly 2,000 locations. Of the 2,000 stores, only about ...
2
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1answer
88 views

Causal impact response time series

I am trying to analyze the effects of an online advertising campaign. The campaign was in market globally except for "Country A". In my response time series, I am using orders from all countries ...
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0answers
37 views

Statistical analysis using small sample size N=11 or 15

I am analyzing firm level data to unpack the cost of producing a renewable energy technology. I have dependent variable as the production cost of the technology, independent variables are three ...
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1answer
63 views

Unconfoundedness in Rubin's Causal Model- Layman's explanation

When implementing Rubin's causal model, one of the (untestable) assumptions that we need is unconfoundedness, which means $$(Y(0),Y(1))\perp T|X$$ Where the LHS are the counterfactuals, the T is the ...
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21 views

Interpreting mediation analysis output

I am trying to carry out some mediation analysis for my dataset hl. Essentially, I am trying to find out the causal mediation effects of weight (wfa: corrected for age) on the total effect that a low ...
3
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1answer
34 views

Causality in Online Classification

I'm using an SVM for an online classification, i.e. datapoints are classified as they come in. Of course, the training has occurred earlier, offline, with an annotated dataset. However, the system ...
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0answers
26 views

How to distinguish a causal chain model from a double effect model?

I am used a linear mixed model on two datasets (two different species), with the same explanatory variable (an environmental variable) for fixed effects. I then select the top 0.1% response ...
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33 views

Exogeneity Interpretation

Consider the following linear regression model:$$Y=X'\beta+u$$ If I wish to estimate this equation by OLS, I have to first think of ways in which the estimator might be biased. More speciifcally, I ...