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Questions tagged [causality]

The relationship between cause and effect.

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AIC for Causal Inference

I read a post explaining why the Akaike Criterion cannot be used for deciding if A cause B or B caused A. I'm curious about a more general case of using AIC for causal inference (with observational ...
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33 views

Causality logic appears reversed. What's the explanation?

In reading Detecting Causality in Complex Ecosystems I came across the following passage: Our alternative approach [...] tests for causation by measuring the extent to which the historical record ...
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1answer
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How to figure out whether a coin is “weighted” with some number of flips

Suppose there's a weighted coin. That coin either lands on heads every 1/10 times it is tossed, or it never lands on heads at all. I don't know whether that coin is the type of coin that lands on ...
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Proof of Berkson's Paradox

I'd be very thankful if someone could help me with the proof of Berkson's Paradox. I found this quite helpful thread which I understand How to prove Berkson's Fallacy?. But I'm actually trying to ...
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If a study indicates no correlation between two variables, does it also indicate a lack of casual relationship? [duplicate]

Of course, correlation does not equal causation. But I am having trouble understanding if there is no correlation between two variables, would this indicate a lack of casual relationship between them ...
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1answer
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Relationship between Causal Calculus (in the sense of the Book of Why) and other existing modeling formalisms?

I am watching this video on youtube: https://youtu.be/zvrcyqcN9Wo?t=2896 about Causal Calculus (CC), namely this section on causal graphs, and it seems to me that this theory of causality is not that ...
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1answer
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Example distribution to match an example?

I am doing exercises from "Causal Inference in Statistics: A Primer", by Pearl et al (2016). In chapter 1.2 there is a training challenge that goes like: In an attempt to estimate the effectiveness ...
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58 views

Term for two variables that are “too close for control”

Sometimes we are tempted to assess a relationship of X1 with Y while controlling for X2, but it would be a mistake, because X2 is not merely correlated with Y -- it is more closely associated than ...
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Instrumental variable predicts endogenous variable in an unexpected direction

I am trying to estimate a causal relationship between two variables. I'm concerned about endogeneity, so I'm using an instrument. The instrument strongly predicts the endogenous regressor, however it ...
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12 views

How to use Bayesian belief Network map/Causality map for segmentation?

I have obtained the causality map for my data. I have an event of interest and the evidences for the event. How do I make the use of this information to come up with segmentation/clustering such that ...
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1answer
56 views

What does it mean if the Average Treatment Effect (ATE) in causal inference is not identifiable?

I read from the following slides on observational studies, pg. 16, Observational Studies, Keio, that given: $$ ATE ≡ E[Y_i(1) − Y_i(0)] $$ They pose the following question: Can we identify the $ATE$...
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1answer
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Difference between Causal Intervention and Causal Mediation

I'm very new to the area of Causality and I would like some clarity as to the difference between the aforementioned terms.
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How to calculate causal effects with repeated exogenous shocks over a time series

A rather frequent problem in causal inference is that we come across various shocks over time and try to measure their impact. In the case of a single shock we can use bayesian methods to predict how ...
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Causality in variance with a BEKK model

I am using a BEKK model in the following form, $$H_t=C^\ast{C^\ast}^\prime+\sum_{i=1}^{m}{A_i\varepsilon_{t-i}\varepsilon_{t-i}A_i^\prime+\sum_{j=1}^{s}{B_jH_{t-j}B_j^\prime}}$$ I first start with a ...
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1answer
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Cause and Effect of one variable on another in python

I have a blog data set, which has essentially many columns such as: ...
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1answer
106 views

In the superpopulation framework of causal inference, what is the necessity of assuming the outcomes, treatment and covariates are jointly iid?

In the textbook "Causal Inference for Statistics" by Rubin and Imbens, the following argument is made on pg. 39: "In part of this text we view our sample of size N as a random sample from an ...
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Effect of age or effect of date of birth ? Is there a fundamental shade between these two?

This question is for who knows about the literature about age effect in educational performance of long-run life outcomes as wages in adulthood for example. Since i read several papers about it (see ...
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Is this causal effect identifiable? It includes a mixture of observational and interventional data with selection effects

The DAG of the model is below. We wish to estimate P(attitude | do(exposed)). moving up from the bottom of the dag: we ...
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1answer
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Survival event simulation with time-varying confounders and treatment-confounder feedback

I'm trying to generate survival data that contain time-varying treatment and time-varying confounding. The goal is to test inverse probability of treatment method developed by Hernan and James Robins. ...
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37 views

Causal model: Controlling for moderating effect of confounder (rather than the confounder itself)

My linear regression model can be described by the following causal graph: I want to explain the sign of the causal effect of $X$ on $Y$, but unfortunately cannot measure $Z$ directly. Thus, I can ...
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1answer
41 views

Is the conditional distribution of Y given X the most we can know about how X “affects” Y?

In his book "Introductory Econometrics", Jeffrey Woolridge states "The most we can know about how X affects Y is contained in the conditional distribution of Y given X". Is this statement true? Would ...
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1answer
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DAGs and all models are wrong motto, what's the implication?

Let's say I have a DAG and I find the right to way to estimate the causal effect of interest (which adjustment to make etc.). Then, I realize my model is wrong. Depending on how my model is wrong, my ...
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37 views

do not need many controls with big data?

I am looking at a paper which uses a large panel data, 1 million observations, a dozen variables. I recall that in a discussion another one has the following comments: In structural models like ...
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2answers
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Confounding variables in experimental study

We conducted a study to analyse the effect of tablet named 'xab' that help smokers to stop smoking. 5500 of smokers are selected. half of them were given different doses of tablet while the other ...
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Deterministic Assignment to Treatment

When estimating causal effects, you want to compare individuals as similar as possible. It is from this need that stems the exchangeability (/ignorability) or conditional exchangeability (/ ...
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1answer
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for a multinomial treatment and binary outcome, what is more appropriate, ATC or ATE?

I need help to choose between ATC and ATE for my analysis with multinomial treatment and binary outcome. In the example below taken from here, it seems that ATT does not sound well for multinomial ...
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4answers
247 views

What is the point of univariate regression before multivariate regression?

I am currently working on a problem in which we have a small dataset and are interested in the causality effect of a treatment on the outcome. My advisor has instructed me to perform a univariate ...
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1answer
54 views

how to calculate manually propensity score weights for multinomial treatments where one of them is baseline

I want to get intuition into the calculation of propensity scores (PS) and inverse probability of treatment weights (IPTW) for a multinomial treatment using multinomial regression. One of the ...
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creating causal model for flight delay prediction [duplicate]

According to DGCA report, if a flight gets delayed, 65% of the times, the reason in reactionary reason. "Reactionary" or "knock-on" delays caused by previous late departures or arrivals. I want to ...
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4answers
54 views

How do I intuitively understand that independence is always symmetric?

Independence between two events, $A$ and $B$, is a symmetric relation, that is, if $P(A \mid B) = P(A)$, then $P(B \mid A) = P(B)$. The proof is very simple and can be found at the ProofWiki. ...
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1answer
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Synthetic control and unobserved confounders

The synthetic control (cohort) method is a very promising approach to causal inference that has been used in a number of interesting studies. It's particularly useful in situations where data are only ...
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1answer
32 views

When should we use the segregated as opposed to the aggregated data?

In the book "Causal Inference In Statistics" by Pearl et al., there is the following problem (study question 1.2.2.) A baseball batter Tim has a better batting average than his teammate Frank. ...
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2answers
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How can we best explain causality for the uninitiated?

How can we best explain causality in layman's terms? There seem to be two main types of causality. One is probabilistic causation, the other is called determinism in philosophic circles or just ...
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1answer
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How to motivate a POLS?

How would you justify the usage of Pooled OLS regression instead of Fixed effects? If I am calculating just correlation between two phenomena, may I get rid of these fixed effects? May I choose to ...
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2answers
27 views

What does it mean to condition on a variable B in causal models?

Given the causal model $A \rightarrow B \rightarrow C$, then $A$ and $C$ are independent conditioned on $B$. What does this mean?
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1answer
28 views

What does it mean for a variable to block a path between other two variables?

What does it mean for a variable $Z$ to "block" the path between variables $X$ and $Y$ in a causal model? What is the formal definition of a "block", and how can I intuitively understand this concept? ...
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1answer
27 views

How to validate the results of bayesian causal network?

There are many ways of validating predicting the results: MSE, MAE, AIC, CV, etc.. But I do not hear any validation way of causality. If the true networks not available, how to make sure the results ...
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Problem in creating causal model for delay analysis

knock-on" are defined as delays caused by previous late departures or arrivals. I am trying to create a model which can help predict the arrival delay of the next flights. But I don't know where to ...
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1answer
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How to separate causality and reverse causality?

For a customer in a grocery store, the greater the number of purchases the longer the shopping path. On the other hand, the longer the shopping path the greater the number of goods to which the ...
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1answer
40 views

Why a path in a causal graph can have edges not all with the same direction?

In a fork, A <- C -> B, A and B are independent given C. We can say that A and B are d-separated or the path between them is blocked by C, given C. So ...
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2answers
59 views

Is individual causal effect identifiable when there is no unmeasured confounder?

From the first section of the causal inference book by Hernan and Robins (link:https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2018/12/hernanrobins_v1.10.37.pdf), I read that the individual ...
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4answers
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Random variables X and Y are dependent conditioned on random variable Z

I intuitively understand the concept of "X and Y are independent conditioned on Z", but I don't get the concept of "X and Y are dependent conditioned on Z". Can you provide some examples which show ...
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1answer
34 views

Why do we care about the joint distribution of the endogenous variables of a causal model?

In general, we can calculate the joint distribution of the endogenous variables of a structural casual model (SCM) as follows $$ P(X, X_1, \dots, X_n) = \prod_i = P(X_i \mid \text{parent}(X_i)) $$ ...
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1answer
54 views

Conterfactual estimation in machine learning model

There are various techniques to build counterfactual estimations of certain variables for linear models in observational studies. Some of those are based on comparing the change in the predicted ...
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2answers
61 views

PCA to recover factors used during data generation. Why doesn't it work?

I often found that the results of a PCA or any kind of factor analysis are interpreted in a "causal" fashion. I.e. if a principal component with high variance explanation is found, this is interpreted,...
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1answer
66 views

Can I use a first difference variable as dependent variable in a panel regression even if it contains both positive and negative values?

Can I still use a first difference variable as the outcome variable to run a panel (say, diff-in-diff) regression? For example, my dependent variable is defined as $Y_{i,t} = M_{i,t} - M_{i,t-1} - P_{...
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1answer
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Propensity score matching: covariate balance

I have one concern about propensity score matching's assumption. It seems that what propensity score is doing is to say that the choice of treatment depends on pre-treatment covariates. Suppose I am ...
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1answer
33 views

Pre.intervention and post.intervention should be contiguous in CausalImpact?

I am running a CausalImpact analysis on a time series and my pre.period goes from 01.01.15 to 30.03.15. I want my post period to be from 15.04.15 to 17.04.15. Is it ok if I create a time series that ...
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28 views

Hypothesis Causality Validation

I have analyzed mortgage data to define a "typical" profile for a first-time home buyer in 2017. Now that I have this information I would like to explain the trends shown in my graphs: why is the ...